@booklet {846, title = {Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets (version 2)}, year = {2025}, abstract = {

In this technical report, we present the experimental findings from evaluating 15 user similarity metrics when applied in sparse collaborative filtering (CF) datasets.

}, keywords = {collaborative filtering, Evaluation, Sparse Datasets, User similarity metrics}, url = {https://soda.dit.uop.gr/sites/soda.dit.uop.gr/files/publications/technical-reports/soda-TR-24001-v2.pdf}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {833, title = {Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets}, number = {TR-24001}, year = {2024}, month = {03/2024}, institution = {SODA lab}, type = {Technical Report}, address = {Tripolis, Greece}, keywords = {collaborative filtering, Evaluation, Sparse Datasets, User similarity metrics}, issn = {SODA-TR-24001}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {839, title = {Improving Recommendation Quality in Collaborative Filtering by Including Prediction Confidence Factors}, booktitle = {Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 24)}, year = {2024}, publisher = {SCITEPRESS Digital Library}, organization = {SCITEPRESS Digital Library}, abstract = {

Collaborative filtering is a prevalent recommender system technique which generates rating predictions based on the rating values given by the users{\textquoteright} near neighbours. Consequently, for each user, the items scoring the highest prediction values are recommended to them. Unfortunately, predictions inherently entail errors, which, in the case of recommender systems, manifest as unsuccessful recommendations. However, along with each rating prediction value, prediction confidence factors can be computed. As a result, items having low prediction confidence factor values, can be either declined for recommendation or have their recommendation priority demoted. In the former case, some users may receive fewer recommended items or even none, especially when using a sparse dataset. In this paper, we present an algorithm that determines the items to be recommended by considering both the rating prediction values and confidence factors of predictions, allowing for predictions with higher confidence factors to outrank predictions with higher value, but lower confidence. The presented algorithm achieves to enhance the recommendation quality, while at the same time retaining the number of recommendations for each user.

}, keywords = {Algorithm, Certainty factors, collaborative filtering, Rating Predictions, Recommender Systems}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Stefanos Ougiaroglou} } @conference {840, title = {Predicting Agricultural Product and Supplies Prices using Artificial Intelligence}, booktitle = {Proceedings of the 16th International Conference on Knowledge Management and Information Systems}, year = {2024}, publisher = {SCITEPRESS Digital Library}, organization = {SCITEPRESS Digital Library}, abstract = {

This work focuses on the prediction of agricultural product and supply prices using historical data and artificial intelligence methods. Agricultural product and supply prices are important for the economy and growth of agriculture. Using modern data analysis and deep learning methods, a forecasting model was developed to help us predict future price trends. The data used include the sales prices of crop products and the purchase prices of agricultural inputs. The developed forecasting methods exhibit high accuracy for predicting the actual prices of products and supplies, with error margins ranging from 0.29\% to 9.8\%, while they can also predict price rises and falls, with respective success rates ranging from 73.29\% to 84.96\%.

}, keywords = {Agricultural Products, Data Integration, deep learning, Forecasting Models, Machine Learning, Price Forecasting}, author = {Ioannis Dionissopoulos and Fotis Assimakopoulos and Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @article {828, title = {Chatbots for Cultural Venues: A Topic-Based Approach}, journal = {Algorithms}, volume = {16}, year = {2023}, month = {jul}, pages = {339}, abstract = {

Digital assistants{\textemdash}such as chatbots{\textemdash}facilitate the interaction between persons and machines and are increasingly used in web pages of enterprises and organizations. This paper presents a methodology for the creation of chatbots that offer access to museum information. The paper introduces an information model that is offered through the chatbot, which subsequently maps the museum{\textquoteright}s modeled information to structures of DialogFlow, Google{\textquoteright}s chatbot engine. Means for automating the chatbot generation process are also presented. The evaluation of the methodology is illustrated through the application of a real case, wherein we developed a chatbot for the Archaeological Museum of Tripolis, Greece.

}, keywords = {chatbots, cultural venues, Museums, RDF triples, topics}, doi = {10.3390/a16070339}, url = {https://doi.org/10.3390/a16070339}, author = {Vasilis Bouras and Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis and Konstantinos Kotis and Angeliki Antoniou and George Lepouras and Manolis Wallace and Vassilis Poulopoulos} } @article {818, title = {Disruptive Technologies for Parliaments: A Literature Review}, journal = {Future Internet}, volume = {15}, year = {2023}, month = {feb}, pages = {66}, abstract = {

Exploitation and use of disruptive technologies, such as the Internet of Things, recommender systems, and artificial intelligence, with an ambidextrous balance, are a challenge, nowadays. Users of the technologies, and stakeholders, could be part of a new organisational model that affects business procedures and processes. Additionally, the use of inclusive participatory organisational models is essential for the effective adoption of these technologies. Such models aim to transform organisational structures, as well. Public organisations, such as the parliament, could utilise information systems{\textquoteright} personalisation techniques. As there are a lot of efforts to define the framework, the methodology, the techniques, the platforms, and the suitable models for digital technologies adoption in public organisations, this paper aims to provide a literature review for disruptive technology inclusive use in parliaments. The review emphasises the assessment of the applicability of the technologies, their maturity and usefulness, user acceptance, their performance, and their correlation to the adoption of relevant innovative, inclusive organisational models. It is argued that the efficient digital transformation of democratic institutions, such as parliaments, with the use of advanced e-governance tools and disruptive technologies, requires strategic approaches for adoption, acceptance, and inclusive service adaptation.

}, keywords = {artificial intelligence, digital transformation, inclusiveness, Internet of Things, parliament, Recommender Systems}, doi = {https://doi.org/10.3390/fi15020066}, url = {https://www.mdpi.com/1999-5903/15/2/66}, author = {Dimitris Koryzis and Dionisis Margaris and Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos} } @article {820, title = {Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets}, journal = {Big Data and Cognitive Computing}, volume = {7}, year = {2023}, month = {mar}, pages = {59}, abstract = {

Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered {\textquotedblleft}close{\textquotedblright} to the user for whom the prediction is being generated have given to the same product or service. In general, {\textquotedblleft}close{\textquotedblright} users for some user u correspond to users that have rated items similarly to u and these users are termed as {\textquotedblleft}near neighbors{\textquotedblright}. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset{\textquoteright}s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method{\textquoteright}s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.

}, keywords = {collaborative filtering, Evaluation, low density datasets, personalization, rating prediction quality, Recommender Systems, reliability}, doi = {10.3390/bdcc7020059}, url = {https://www.mdpi.com/2504-2289/7/2/59}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos and Stefanos Ougiaroglou} } @conference {830, title = {A User Interface for Tuning QoS parameters in Recommendation-Based Business Process Scenario Adaptation}, booktitle = {Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023)}, year = {2023}, abstract = {

The Web Services Business Process Execution Language (BPEL) is a special-purpose language that orchestrates web services into a high-level business process. A typical BPEL scenario contains invocations to preselected web services, along with their parameters. However, many recent research works support dynamic service selection, based on user-set policies and criteria. Furthermore, users may request a service recommendation, in which case functionally equivalent service offerings by different providers will be considered by the personalization module. Along with the recommendation request, users provide the policy parameters, which include minimum and maximum bounds for the non-functional attributes concerning the service, and the system exploits these bounds to select and use the optimal candidate services. However, in many real-life cases, a person will accept/purchase a product or a service that exceeds the threshold(s) that initially he/she has set, e.g. if the overhead is marginal or the offer is deemed appealing, or no satisfactory service candidates are identified using the initial settings. In this paper, we present and evaluate a specialized User Interface that allows the user to review service candidates marginally exceeding the specified bounds, and consider them while making the final service selection.

}, keywords = {adaptation, business processes, Personalisation, Recommendation, Recommender Systems, UI, Web services, WS-BPEL}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {810, title = {Anchoring Effect Mitigation for Complex Recommender System Design}, booktitle = {Proceedings of the 2022 HCI International Conference}, year = {2022}, keywords = {Information/knowledge design/visualization, mental-model, Metaphor, navigation design, Service Design}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {812, title = {Persona Finetuning for Online Gaming using Personalisation Techniques}, booktitle = {Proceedings of the 2022 HCII Conference}, year = {2022}, abstract = {

Automatic persona generation has been shown to have specific measurable benefits for application creators and users. In most situations, personas are adequately descriptive and diversified to achieve user type accuracy and coverage. For specific market segments, such as online gaming, using personas may accurately describe existing user base but not changing habit and need that are introduced by the fluidity of the offerings and the delivery methods. Changes in the ways that applications are marketed, such as new payment methods, for example, subscription models, pay-to-play and pay-to-win, payment-driven-gamification, seriously affect user needs and result in direct impact on user acceptance. This work utilises structured user needs from online gaming players to augment personas using personalisation techniques. The personas are finetuned and dediversified to result in concise personas, based on user needs that successfully convey information for creators and users alike.

}, keywords = {collaborative filtering, Data-driven methods, Diversification, Online Gaming, Persona, Personalisation, Usability evaluation, User study}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Kostas N. Koutrakis and Panos Philippopoulos and Costas Vassilakis} } @article {813, title = {On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets}, journal = {Information}, volume = {13}, year = {2022}, month = {jun}, pages = {302}, abstract = {

The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user{\textendash}item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system{\textquoteright}s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.

}, keywords = {accuracy, collaborative filtering, Evaluation, Personalisation, Rating prediction, Recommender Systems, reliability, Sparse Datasets}, doi = {https://doi.org/10.3390/info13060302}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos} } @article {803, title = {Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering}, journal = {Applied Sciences}, volume = {11}, year = {2021}, month = {sep}, pages = {8369}, abstract = {

In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users{\textquoteright} ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of {\textquoteleft}black sheep near neighbours{\textquoteright}, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.

}, keywords = {black sheep users, collaborative filtering, Evaluation, rating prediction accuracy}, doi = {10.3390/app11188369}, url = {https://doi.org/10.3390/app11188369}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @inbook {808, title = {Identifying Reliable Recommenders in Users{\textquoteright} Collaborating Filtering and Social Neighbourhoods}, booktitle = {Lecture Notes in Social Networks}, year = {2021}, pages = {51{\textendash}76}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Recommender systems increasingly use information sourced from social networks to improve the quality of their recommendations. However, both recommender systems and social networks exhibit phenomena under which information for certain users or items is limited, such as the cold start and the grey sheep phenomena in collaborative filtering systems and the isolated users in social networks. In the context of a social network-aware collaborative filtering, where the collaborating filtering- and social network{\textendash}based neighbourhoods are of varying density and utility for recommendation formulation, the ability to identify the most reliable recommenders from each neighbourhood for each user and appropriately combine the information associated with them in the recommendation computation process can significantly improve the quality and accuracy of the recommendations offered. In this chapter, we report on our extensions on earlier works in this area which comprise of (1) the development of an algorithm for discovering the most reliable recommenders of a social network recommender system and (2) the development and evaluation of a new collaborative filtering algorithm that synthesizes the opinions of a user{\textquoteright}s identified recommenders to generate successful recommendations for the particular user. The proposed algorithm introduces significant gains in rating prediction accuracy (4.9\% on average, in terms of prediction MAE reduction and 4.2\% on average, in terms of prediction RMSE reduction) and outperforms other algorithms. The proposed algorithm, by design, utilizes only basic information from the collaborative filtering domain (user{\textendash}item ratings) and the social network domain (user relationships); therefore, it can be easily applied to any social network recommender system dataset.

}, keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Rating prediction, Recommender Systems, Social Networks}, doi = {10.1007/978-3-030-67044-3_3}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @inbook {807, title = {Safe Travelling Period Recommendation to High Attack Risk European Destinations Based on Past Attack Information}, booktitle = {Lecture Notes in Social Networks}, year = {2021}, pages = {77{\textendash}100}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Terrorism is a significant deterrent for tourism. It affects both visitors and local citizens and personnel of a country or area. On one hand, the potential visitor will probably avoid travelling to a high attack risk country, due to safety reasons, hence will miss the opportunity to visit it, and, on the other hand, the country{\textquoteright}s tourism will decline. This work addresses the aforementioned problem by (1) showing that relatively safe visiting periods for high attack risk European countries can be predicted with high accuracy, using limited information, comprising of attack and fatality data from the past years, which are widely available, and (2) developing an algorithm that recommends relatively safe periods to potential travellers. The results of this work will be useful for tourists, visitors, businesses and operators, as well as relevant stakeholders and actors.

}, keywords = {Evaluation, Recommendation algorithm, Risk Calculation, Safety Perception, Safety prediction, Terrorist Attacks, tourism}, doi = {10.1007/978-3-030-67044-3_3}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @article {771, title = {An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy}, journal = {IEEE Access}, volume = {8}, year = {2020}, pages = {68301{\textendash}68310}, abstract = {When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced. However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all. Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system. In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users{\textquoteright} ratings on items and users{\textquoteright} social relations. The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user{\textquoteright}s social network and collaborative filtering neighborhoods. Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy. Furthermore, the proposed algorithm can be easily implemented in recommender systems}, keywords = {Performance evaluation, Recommender Systems, Social computing}, issn = {2169-3536}, doi = {10.1109/access.2020.2981567}, author = {Dionisis Margaris and Anna Kobusinska and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {779, title = {An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings}, journal = {Algorithms}, volume = {13}, year = {2020}, month = {07/2020}, pages = {174}, abstract = {

Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.

}, keywords = {collaborative filtering, density enrichment, derived ratings, rating prediction accuracy, rating prediction coverage, Recommender Systems, robust predictions, Sparse Datasets}, doi = {https://doi.org/10.3390/a13070174}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Gregory Karagiorgos and Costas Vassilakis} } @article {782, title = {Data-Assisted Persona Construction Using Social Media Data}, journal = {Big Data and Cognitive Computing}, volume = {4}, year = {2020}, month = {aug}, pages = {21}, abstract = {

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.

}, keywords = {Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design}, doi = {10.3390/bdcc4030021}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @article {723, title = {A Fine-grained Social Network Recommender System}, journal = {Social Network Analysis and Mining}, volume = {10}, year = {2020}, abstract = {Recommender systems have greatly evolved in recent years and have become an integral part of the Web. From e-commerce sites to mobile apps, our daily routine revolves around a series of {\textquotedblleft}small{\textquotedblright} decisions that are influenced by such recommendations. In a similar manner, online social networks recommend only a subset of the massive amount of content published by a user{\textquoteright}s friends. However, the prevalent approach for the content selection process in such systems is driven by the amount of interaction between the user and the friend who published the content. As a result, content of interest is often lost due to weak social ties. In this paper, we present a fine-grained recommender system for social ecosystems, designed to recommend media content (e.g., music videos, online clips) published by the user{\textquoteright}s friends. The system design was driven by the findings of our qualitative user study that explored the value and requirements of a recommendation component within a social network. The core idea behind the proposed approach was to leverage the abundance of preexisting information in each user{\textquoteright}s account for creating interest profiles, to calculate similarity scores at a fine-grained level for each friend. The intuition behind the proposed method was to find consistent ways to obtain information representations that can identify overlapping interests in very specific sub-categories (e.g., two users{\textquoteright} music preferences may only coincide on hard rock). While the system is intended as a component of the social networking service, we developed a proof-of-concept implementation for Facebook and explored the effectiveness of our underlying mechanisms for content analysis. Our experimental evaluation demonstrates the effectiveness of our approach, as the recommended content of interest was both overlooked by the existing Facebook engine and not contained in the users{\textquoteright} Facebook News Feed. We also conducted a user study for exploring the usability aspects of the prototype and found that it offers functionality that could significantly improve user experience in popular services.}, keywords = {Content Analysis, Interest Profiles, Recommender Systems, Social Networks, Usability}, issn = {1869-5469}, doi = {10.1007/s13278-019-0621-7}, author = {Markos Aivazoglou and Antonios Roussos and Dionisis Margaris and Costas Vassilakis and Sotiris Ioannidis and Jason Polakis and Dimitris Spiliotopoulos} } @article {margaris-IJBDI-2019-b, title = {Improving collaborative filtering{\textquoteright}s rating prediction accuracy by considering users{\textquoteright} dynamic rating variability}, journal = {International Journal of Big Data Intelligence}, volume = {7}, year = {2020}, chapter = {59--71}, abstract = {

Users that populate ratings databases, follow different marking practices, in the sense that some are stricter, while others are more lenient. Similarly, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some users may be entering ratings close to their mean, while other users may be entering more extreme ratings, close to the limits of the rating scale. While this aspect has been recently addressed through the computation and exploitation of an overall rating variability measure per user, the fact that user rating practices may vary along the user{\textquoteright}s rating history time axis may render the use of the overall rating variability measure inappropriate for performing the rating prediction adjustment. In this work, we (1) propose an algorithm that considers two variability metrics per user, the global (overall) and the local one, with the latter representing the user{\textquoteright}s variability at prediction time, (2) present alternative methods for computing a user{\textquoteright}s local variability and (3) evaluate the performance of the proposed algorithm in terms of rating prediction quality and compare it against the state-of-the-art algorithm that employs a single variability metric in the rating prediction computation process.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, prediction accuracy, users{\textquoteright} ratings dynamic variability}, doi = {10.1504/ijbdi.2020.107373}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-IJBDI-2019-a, title = {Improving collaborative filtering{\textquoteright}s rating prediction coverage in sparse datasets by exploiting the {\textquotedblleft}friend of a friend{\textquotedblright} concept}, journal = {International Journal of Big Data Intelligence}, volume = {7}, year = {2020}, chapter = {47}, abstract = {

Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbours{\textquotedblright} and recommendations for a user are based on her near neighbours{\textquoteright} ratings. However, for a number of users no near neighbours can be found, a problem termed as the {\textquotedblleft}gray sheep{\textquotedblright} problem. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose an algorithm for alleviating this problem by exploiting the friend of a friend (FOAF) concept. The proposed algorithm, CFfoaf, has been evaluated against eight widely used sparse datasets and under two widely used collaborative filtering correlation metrics, namely the Pearson Correlation Coefficient and the Cosine Similarity and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time improving rating prediction quality.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, friend-of-a-friend, Pearson Correlation Coefficient, prediction accuracy, Recommender Systems, Sparse Datasets}, issn = {2053-1389}, doi = {10.1504/ijbdi.2020.106178}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {784, title = {MOOC Accessibility from the Educator Perspective}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

This work presents the universal access design principles and methods for natural language communication design in e-learning for the disabled. It unfolds a theoretical perspective to the design-for-all methodology and provides a framework description for technologies for creating accessible content for educational content communication. Main concerns include the problem identification of design issues for universal accessibility of spoken material, the primary pedagogical aspects that such content implementation should follow upon, as well as look into the state of the most popular e-learning platforms for which educators create and communicate educational content in an e-learning environment. References to massive open online course platform types of content that exist at the moment are examined in order to understand the challenges of bridging the gap between the modern design of rich courses and universal accessibility. The paper looks into the existing technologies for accessibility and a frame for analysis, including methodological and design issues, available resources and implementation using the existing technologies for accessibility and the perception of the designer as well as the user standpoint. Finally, a study to inform and access how potential educators may perceive the accessibility factor shows that accessible content is a major requirement toward a successful path to universally accessible e-learning.

}, keywords = {design-for-all, interactive e-learning, MOOC, speech processing, technologies for accessibility, universal accessibility}, url = {10.1007/978-3-030-60149-2_10}, author = {Dimitris Spiliotopoulos and Vassilis Poulopoulos and Dionisis Margaris and Eleni Makri and Costas Vassilakis} } @article {781, title = {Multimodal Interaction: Correlates of Learners{\textquoteright} Metacognitive Skill Training Negotiation Experience}, journal = {Information}, volume = {11}, year = {2020}, month = {jul}, pages = {381}, abstract = {

Metacognitive training reflects knowledge, consideration and control over decision-making and task performance evident in any social and learning context. Interest in understanding the best account of effective (win-win) negotiation emerges in different social and cultural interactions worldwide. The research presented in this paper explores an extended study of metacognitive training system during negotiation using an embodied conversational agent. It elaborates on the findings from the usability evaluation employing 40 adult learners pre- and postinteraction with the system, reporting on the usability and metacognitive, individual- and community-level related attributes. Empirical evidence indicates (a) higher levels of self-efficacy, individual readiness to change and civic action after user-system experience, (b) significant and positive direct associations between self-efficacy, self-regulation, interpersonal and problem-solving skills, individual readiness to change, mastery goal orientation and civic action pre- and postinteraction and (c) gender differences in the perceptions of system usability performance according to country of origin. Theoretical and practical implications in tandem with future research avenues are discussed in light of embodied conversational agent metacognitive training in negotiation.

}, keywords = {big data, Cultural Events, Persona, personalization, Topic Modelling, Usability, User Experience, User Interface Design}, doi = {10.3390/info11080381}, author = {Dimitris Spiliotopoulos and Eleni Makri and Costas Vassilakis and Dionisis Margaris} } @conference {786, title = {Policy Making Analysis and Practitioner User Experience}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

This article presents the work on social media analysis-driven policy-making platforms that are powered by classic social media analysis technologies, such as policy modelling, linguistic analysis, opinion mining, sentiment analysis and information visualization. The approach examines the user design perspective towards user experience in policymaking for all the innovative modules used. The technology behind such complex task is presented while the resulting platform is appraised on the potential for real world application. The findings drive the development and the requirements for the summative usability assessment tests. We also report on the level the practitioners adopted the policy formulation tools.

}, keywords = {Content Analysis, natural language interfaces, opinion mining, policy making, social network analysis, User Experience}, doi = {10.1007/978-3-030-60114-0_29}, author = {Dimitris Koryzis and Fotios Fitsilis and Dimitris Spiliotopoulos and Theocharis Theocharopoulos and Dionisis Margaris and Costas Vassilakis} } @article {770, title = {A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems}, journal = {Big Data and Cognitive Computing}, volume = {4}, year = {2020}, month = {4/2020}, pages = {6}, abstract = {This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.}, keywords = {cultural experience, intelligent interaction, Mixed Reality, semantics, Usability}, doi = {https://doi.org/10.3390/bdcc4020006}, author = {Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos and Dionisis Margaris and Vlasios Kasapakis and Christos-Nikolaos Anagnostopoulos and Georgios Santipantakis and George A. Vouros and Theodore Kotsilieris and Volha Petukhova and Andrei Malchanau and Ioanna Lykourentzou and Kaj Michael Helin and Artem Revenko and Nenad Gligoric and Boris Pokric} } @conference {777, title = {Semantics-Driven Conversational Interfaces for Museum Chatbots}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction, volume "Culture and Computing"}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

This work addresses the challenges of creating usable and personalized conversational interfaces for broad, yet applicable, domains that require user engagement and learning, such as museum chatbots. Whether the chatbots are standalone or coupled with virtual agents or real-life robots, the functional requirements for interaction that targets specific learning aspects would be expected to be more or less similar. This work reports on experimental semantics-driven conversational interface design for chatbots in museum settings, targeting visitors to converse about exhibits and learn information about their style, the artists, the era, and other aspects related to them. Depending on the semantics (presentation, learning, exploration), chatbot scenarios were designed and evaluated by participants in a formative evaluation. The evaluation show that user requirement perception manifests in expectations on the semantic level, instead of just the technical level. The results between the scenarios are compared to see how the semantics considered for the design transferred to the implementation and to the user perception.

}, keywords = {chatbots, conversational interfaces, cultural technology, semantics}, doi = {10.1007/978-3-030-50267-6_20}, author = {Dimitris Spiliotopoulos and Konstantinos Kotis and Costas Vassilakis and Dionisis Margaris} } @conference {778, title = {{\textquotedblleft}Talking{\textquotedblright} Triples to Museum Chatbots}, booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction, volume "Culture and Computing"}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

The paper presents recent work on the design and development of AI chatbots for museums using Knowledge Graphs (KGs). The utilization of KGs as a key technology for implementing chatbots raises not only issues related to the representation and structuring of exhibits{\textquoteright} knowledge in suitable formalism and models, but also issues related to the translation of natural language dialogues to and from the selected technology for the formal representation and structuring of information and knowledge. Moreover, such a translation must be as transparent as possible to visitors, towards a realistic human-like question-answering process. The paper reviews and evaluates a number of recent approaches for the use of KGs in developing AI chatbots, as well as key tools that provide solutions for natural language translation and the querying of Knowledge Bases and Linked Open Data sources. This evaluation aims to provide answers to issues that are identified within the proposed MuBot approach for designing and implementing AI chatbots for museums. The paper also presents Cretan MuBot, the first experimental KG/Ontology-based AI chatbot of the MuBot Platform, which is under development in the Heracleum Archaeological Museum.

}, keywords = {chatbots, knowledge graphs, Museums, NLP, RDF triples}, doi = {10.1007/978-3-030-50267-6_22}, author = {Savvas Varitimiadis and Konstantinos Kotis and Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris} } @conference {785, title = {A User Interface for Personalized Web Service Selection in Business Processes}, booktitle = {Lecture Notes in Computer Science}, year = {2020}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Nowadays, due to the huge volume of information available on the web, the need for personalization is more than necessary. Choosing the right information for each user is as important as the way this information is presented to him or her. Currently, user-triggered recommendation requests for web services are implemented as an automatic recommendation based on parametric computation. This work reports on a specialized user interface for business processes, where writing code entails invocation of business process information. The paper presents the user interface design for Personalized Web Service Selection in Business Process scenario execution and the user evaluation by business process engineers.

}, keywords = {adaptation, business processes, personalization, user interface, Web services, WS-BPEL}, doi = {10.1007/978-3-030-60152-2_41}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Gregory Karagiorgos} } @article {775, title = {What makes a review a reliable rating in recommender systems?}, journal = {Information Processing \& Management}, volume = {57}, year = {2020}, month = {04/2020}, pages = {102304}, abstract = {

The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews -which in the context of many social networks are in abundance and significantly outnumber numeric ratings- need to be converted to numeric ratings. While numerous approaches exist that calculate a user{\textquoteright}s rating based on the respective textual review, all such approaches may introduce errors, in the sense that the process of rating calculation based on textual reviews involves an uncertainty level, due to the characteristics of the human language, and therefore the calculated ratings may not accurately reflect the actual ratings that the corresponding user would enter. In this work (1) we examine the features of textual reviews, which affect the reliability of the review-to-rating conversion procedure, (2) we compute a confidence level for each rating, which reflects the uncertainty level for each conversion process, (3) we exploit this metric both in the users{\textquoteright} similarity computation and in the prediction formulation phases in recommender systems, by presenting a novel rating prediction algorithm and (4) we validate the accuracy of the presented algorithm in terms of (i) rating prediction accuracy, using widely-used recommender systems datasets and (ii) recommendations generated for social network user satisfaction and precision, where textual reviews are abundant.

}, keywords = {collaborative filtering, Confidence level, Feature selection, Rating prediction using textual information, Recommender Systems}, doi = {https://doi.org/10.1016/j.ipm.2020.102304}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {717, title = {Citizen Engagement for Transparent and Accountable Policy Modelling}, booktitle = {Proceedings of the 11th International Conference on Management of Digital EcoSystems (MEDES{\textquoteright}19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {In the modern information society, learning is no longer just about obtaining factual knowledge, but more about general skills on how and where to apply available knowledge and obtain new knowledge in order to solve new problems. Such skills include abilities to connect and organize ideas, fill gaps in knowledge structures, evaluate evidence, argue with new information, test and modify, predict, clarify, generate questions, learn new concepts, make unexpected connections, reflect, analyze, synthesize and loop back. This work presents the Immersion framework, a digital ecosystem for adaptive smart learning environments for interactive mixed reality driven methods to foster learners{\textquoteright} self-regulating skill development.}, keywords = {Game-based Interaction, Learner Collaboration, Mixed Reality, Mobile Learning, Self-regulated Learning, Technology Enhanced Learning}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis} } @conference {spiliotopoulos_2019_asonam, title = {Data-driven Country Safety Monitoring Terrorist Attack Prediction}, booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)}, year = {2019}, month = {aug}, abstract = {Terrorism is a key risk for prospective visitors of tourist desti-nations. This work reports on the analysis of past terrorist at-tack data, focusing on tourist-related attacks and attack types in Mediterranean EU area and the development of algorithms to predict terrorist attack risk levels. Data on attacks in 10 coun-tries have been analyzed to quantify the threat level of tourism-related terrorism based on the data from 2000 to 2017 and for-mulate predictions for subsequent periods. Results show that predictions on potential target types can be derived with ade-quate accuracy. Such results are useful for initiating, shifting and validating active terrorism surveillance based on real pre-dicted attack and target types per country.}, keywords = {Data Analysis, Risk Calculation, Safety Perception, Terrorist Attacks, Threat Ranking}, author = {Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris} } @article {SODA-TR-19003, title = {Experimental results for considering Item Adoption Eagerness Information in Collaborative Filtering{\textquoteright}s Rating Prediction}, year = {2019}, month = {07/2019}, publisher = {Software and Database Systems Lab, University of the Peloponnese}, type = {Technical report}, address = {Tripolis}, abstract = {In this technical report, we present the experimental findings from applying an algorithm that considers Item Adoption Eagerness Information in the rating prediction formulation process, in order to increase rating prediction quality in Collaborative Filtering (CF). To this end, the algorithm is applied to seven datasets, which are widely used in recommender system (RS) research. The results indicate that the above algorithm introduces considerable prediction accuracy gains. }, url = {https://soda.dit.uop.gr/?q=TR-19003}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {margarisIISA_CIRPaC, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbors{\textquotedblright} and recommendations for a user are based on his near neighbors{\textquoteright} ratings. In order to measure similarity between users, so as to determine a user{\textquoteright}s NNs, a similarity metric is used. Insofar, similarity metrics proposed in the literature either consider all user ratings equally or take into account temporal variations within the users{\textquoteright} or items{\textquoteright} ratings history. However users{\textquoteright} ratings are co-shaped according to the experiences that they had in the past; therefore if two users enter similar (or dissimilar) ratings for an item while having experienced {\textendash}to a large extent the same items in the past, this constitutes stronger evidence about user similarity (or dissimilarity). Insofar however, no similarity metric takes into account this aspect. In this work, we (1) propose an algorithm that considers the common item rating past in the rating prediction computation process, aiming to improve rating prediction quality, and (2) evaluate the proposed algorithm against seven widely used datasets, both dense and sparse, and considering two widely used user similarity metrics.}, keywords = {collaborative filtering, Common Item Rating Past Criterion, Cosine Similarity, Evaluation, Items{\textquoteright} Rating Sequence, Pearson Correlation Coeffi-cient}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {margaris_iisa2019_VNNs, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Collaborative filtering creates personalized recommendations by considering ratings entered by users. Collaborative filtering algorithms initially detect users whose likings are alike, by ex-ploring the similarity between ratings that have insofar been submitted. Users having a high degree of similarity regarding their ratings are termed near neighbors, and in order to formu-late a recommendation for a user, her near neighbors{\textquoteright} ratings are extracted and form the basis for the recommendation. Col-laborative filtering algorithms however exhibit the problem commonly referred to as {\textquotedblleft}gray sheep{\textquotedblright}: this pertains to the case where for some users no near neighbors can be identified, and hence no personalized recommendations can be computed. The {\textquotedblleft}gray sheep{\textquotedblright} problem is more severe in sparse datasets, i.e. datasets where the number of ratings is small, compared to the number of items and users. In this paper, we address the {\textquotedblleft}gray sheep{\textquotedblright} problem by introducing the concept of virtual near neighbors and a related algorithm for their creation on the basis of the existing ones. We evaluate the proposed algorithm, which is termed as CFVNN, using eight widely used datasets and consid-ering two correlation metrics which are widely used in Collabo-rative Filtering research, namely the Pearson Correlation Coef-ficient and the Cosine Similarity. The results show that the pro-posed algorithm considerably leverages the capability of a Col-laborative Filtering system to compute personalized recommen-dations in the context of sparse datasets, tackling thus efficiently the {\textquotedblleft}gray sheep{\textquotedblright} problem. In parallel, the CFVNN algorithm achieves improvements in rating prediction quality, as this is expressed through the Mean Absolute Error and the Root Mean Square Error metrics.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Sparse Datasets, Virtual Near Neighbors}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {716, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information}, booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, who are termed as {\textquotedblleft}near neighbors" and recommendations for a user, are based on his near neighbors{\textquoteright} ratings. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as {\textquotedblleft}Early Adopters", who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we (1) propose an algorithm that considers the eagerness shown by users to adopt products, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, item adoption eagerness, Pearson Correlation Coefficient, rating prediction quality}, isbn = {978-1-4503-6934-3}, doi = {10.1145/3350546.3352544}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {spiliot_iisa2019, title = {A Methodology for Generated Text Annotation for High Quality Speech Synthesis}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Natural Language Generators may produce linguistically en-riched text descriptions that can lead to significantly improved quality of speech synthesis. There are cases, however, where either the generator modules produce pieces of non-analysed, non-annotated plain text, or such modules are not available at all. Moreover, the language analysis is restricted by the usually limited domain coverage of the generator. For those cases the enriched input to the speech synthesizer needs to be produced from plain text in order to maintain speech quality. This work reports on the method for automatic domain dependent annota-tion of plain text, through the utilisation of the linguistic infor-mation from rich generated text. The synthetic speech from the resulting prosody models is evaluated by human participants showing annotation results for plain text quite on par with the rich generated text. This leads to improved perceived natural-ness of the synthesized speech.}, keywords = {Natural language generation, Natural language processing, Prosody enrichment, Semantic feature annotation, Text-to-speech}, author = {Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris and Konstantinos Kotis} } @conference {718, title = {A Mixed-reality Interaction-driven Game-based Learning Framework}, booktitle = {Proceedings of the 11th International Conference on Management of Digital EcoSystems (MEDES{\textquoteright}19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {This work presents a platform for linked legislative data to engage citizens in transparent and effective democracies. With a focus on scaling up participatory approaches from local to national level, the approach extends well established and open source tools and technologies, to build mobile monitoring and analysis tools that increase transparency of law-making and implementation to citizens. This is achieved by combining open data and open services with user and citizen generated content, in order to address citizen{\textquoteright}s needs in the context of open government. Data and feeds from trusted sources are interconnected with new and re-purposed data feeds generated by users via the social web to form a meaningful, searchable, customizable, re-usable and open data-focused personalised mobile public service approach. The framework exploits the social aspects of open data, as well as the training of users, citizens and public servants to be able to understand and demand useful public open data, as well as facilitate the opening of more data.}, keywords = {Accountability, Citizen Engagement, e-government, Legislation, Mobile Public Services, Natural language processing, Policy Modelling, Transparency}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis and Volha Petukhova and Konstantinos Kotis} } @conference {Margaris_2019_asonam, title = {Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising}, booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)}, year = {2019}, month = {aug}, abstract = {Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users{\textquoteright} aspects are taken into consid-eration. However, social network information may be unavaila-ble since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited col-laborative filtering information, comprised only of users{\textquoteright} ratings on items, with limited social network information, com-prised only of users{\textquoteright} social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.}, keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Online Advertising, Pearson Correlation Coefficient, Social Networks}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {margaris2018tldks, title = {Enhancing Rating Prediction Quality Through Improving the Accuracy of Detection of Shifts in Rating Practices}, journal = {Lecture Notes in Computer Science}, volume = {10940 (LNCS)}, number = {XXXVII}, year = {2018}, pages = {151{\textendash}191}, publisher = {Springer Berlin Heidelberg}, abstract = {The most widely used similarity metrics in collaborative filtering, namely the Pearson Correlation and the Adjusted Cosine Similarity, adjust each individual rating by the mean of the ratings entered by the specific user, when computing similarities, due to the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, a user{\textquoteright}s rating practices change over time, i.e. a user could start as lenient and subsequently become stricter or vice versa; hence by relying on a single mean value per user, we fail to follow such shifts in users{\textquoteright} rating practices, leading to decreased rating prediction accuracy. In this work, we present a novel algorithm for calculating dynamic user averages, i.e. time-in-point averages that follow shifts in users{\textquoteright} rating practices, and exploit them in both user-user and item-item collaborative filtering implementations. The proposed algorithm has been found to introduce significant gains in rating prediction accuracy, and outperforms other dynamic average computation approaches that are presented in the literature.}, doi = {10.1007/978-3-662-57932-9_5}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris2018a, title = {Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems}, journal = {Informatics}, volume = {5}, number = {2}, year = {2018}, month = {apr}, pages = {21 pages}, abstract = {One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user{\textquoteright}s interests, thus improving prediction quality.}, keywords = {collaborative filtering, concept drift, Evaluation, Recommender Systems, shift of interest, Social Networks}, doi = {10.3390/informatics5020021}, url = {http://www.mdpi.com/2227-9709/5/2/21}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margarisDatacom2018a, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Considering Users{\textquoteright} Rating Variability}, booktitle = {2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)}, year = {2018}, publisher = {IEEE}, organization = {IEEE}, abstract = {When rating predictions are computed in user-user collaborative filtering, each individual rating is typically adjusted by the mean of the ratings entered by the specific user. This practice takes into account the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some user may be entering ratings close to her mean, while another user may be entering more extreme ratings, close to the limits of the rating scale. In this work, we (1) propose an algorithm that considers users{\textquoteright} ratings variability in the rating prediction computation process, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets considering three widely used variability measures and two user similarity metrics. The proposed algorithm, using the {\textquotedblleft}mean absolute deviation around the mean{\textquotedblright} variability measure, has been found to intro-duce considerable gains in rating prediction accuracy, in every dataset and under both user similarity metrics tested.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Users{\textquoteright} Ratings Variability}, doi = {10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00145}, url = {https://ieeexplore.ieee.org/document/8512012}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margarisDatacom2018b, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets by Exploiting User Dissimilarity}, booktitle = {2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)}, year = {2018}, month = {aug}, publisher = {IEEE}, organization = {IEEE}, abstract = {Collaborative filtering systems analyze the ratings databases to identify users with similar likings and preferences, termed as near neighbors, and then generate rating predictions for a user by examining the ratings of his near neighbors for items that the user has not yet rated; based on rating predictions, recommenda-tions are then formulated. However, these systems are known to exhibit the {\textquotedblleft}gray sheep{\textquotedblright} problem, i.e. the situation where no near neighbors can be identified for a number of users, and hence no recommendation can be formulated for them. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose a method for alleviating this problem by exploiting user dissimilarity, under the assumption that if some users have exhibited opposing preferences in the past, they are likely to do so in the future. The proposed method has been eval-uated against seven widely used datasets and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time maintaining or slightly improving rating prediction quality.}, keywords = {collaborative filtering, Evaluation, Negative Correlation, Pearson Correlation Coefficient, Sparse Datasets, User-User Correlation}, doi = {10.1109/dasc/picom/datacom/cyberscitec.2018.00150}, url = {https://ieeexplore.ieee.org/document/8512017}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-tldks, title = {Enhancing User Rating Database Consistency Through Pruning}, journal = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV}, volume = {LNCS, volume 10620}, number = {Special issue on Consistency and Inconsistency in Data-centric Applications}, year = {2017}, pages = {33{\textendash}64}, publisher = {Springer Berlin Heidelberg}, abstract = {Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose. In this paper, we present a pruning technique that removes old aged user behavior data from the ratings database, which are bound to correspond to invalidated preferences of the user. Through pruning (1) inconsistencies are removed and data quality is upgraded, (2) better rating prediction generation times are achieved and (3) the ratings database size is reduced. We also propose an algorithm for determining the amount of pruning that should be performed, allowing the tuning and operation of the pruning algorithm in an unsupervised fashion. The proposed technique is evaluated and compared against seven aging algorithms, which reduce the importance of aged ratings, and a state-of-the-art pruning algorithm, using datasets with varying characteristics. It is also validated using two distinct rating prediction computation strategies, namely collaborative filtering and matrix factorization. The proposed technique needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values), can be used in all rating databases that include a timestamp and has been proved to be effective in any size of users-items database and under two rating prediction computation strategies. Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author{\textquoteright}s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.}, keywords = {Collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services}, doi = {10.1007/978-3-662-55947-5_3}, url = {https://link.springer.com/article/10.1007\%2Fs11761-017-0216-y}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-soca-17, title = {Exploiting Internet of Things information to enhance venues{\textquoteright} recommendation accuracy}, journal = {Service Oriented Computing and Applications}, volume = {todb}, year = {2017}, month = {aug}, pages = {393{\textendash}409}, publisher = {Springer Nature}, abstract = {In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theatres, etc.), such as the user{\textquoteright}s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.}, keywords = {collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services}, doi = {10.1007/s11761-017-0216-y}, url = {https://link.springer.com/article/10.1007/s11761-017-0216-y}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris-cbi17, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Considering Shifts in Rating Practices}, booktitle = {Proceedings of the 19th IEEE International Conference on business informatics (CBI17)}, year = {2017}, month = {07}, publisher = {IEEE}, organization = {IEEE}, abstract = {Users that populate ratings databases, such as IMDB, follow different marking practices, in the sense that some are stricter, while others are more lenient. This aspect has been captured by the most widely used similarity metrics in collaborative filtering, namely the Pearson Correlation and the Adjusted Cosine Similarity, which adjust each individual rating by the mean of the ratings entered by the specific user, when computing similarities. However, relying on the mean value presumes that the users{\textquoteright} marking practices remain constant over time; in practice though, it is possible that a user{\textquoteright}s marking practices change over time, i.e. a user could start as strict and subsequently become lenient, or vice versa. In this work, we propose an approach to take into account marking practices shifts by (1) introducing the concept of dynamic user rating averages which follow the users{\textquoteright} marking practices shifts, (2) presenting two alternative algorithms for computing a user{\textquoteright}s dynamic averages and (3) performing a comparative evaluation among these two algorithms and the classic static average (unique mean value) that the Pearson Correlation uses.}, doi = {10.1109/CBI.2017.24}, url = {https://ieeexplore.ieee.org/abstract/document/8010718/}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris-iscc17, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality in Dense Datasets, by Pruning Old Ratings}, booktitle = {Proceedings of the 22nd IEEE Symposium on Computers and Communications (ISCC17)}, year = {2017}, month = {07}, publisher = {IEEE}, organization = {IEEE}, abstract = {In this paper, we introduce a pruning algorithm which removes aged user ratings from the rating database used by collaborative filtering algorithms, in order to (1) improve prediction quality and (2) minimize the rating database size, as well as the rating prediction generation time. The proposed algorithm needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values) and can be used with all rating databases that include a timestamp. Furthermore, we propose and validate a method for identifying the most prominent combination of a pruning algorithm and a pruning level for datasets, allowing thus to perform the selection of pruning algorithm and pruning level in an unsupervised fashion.}, doi = {10.1109/ISCC.2017.8024683}, url = {https://ieeexplore.ieee.org/document/8024683/}, author = {Dionisis Margaris and Costas Vassilakis} } @inbook {marg16d, title = {Knowledge-Based Leisure Time Recommendations in Social Networks}, booktitle = {Current Trends on Knowledge-Based Systems}, series = {Intelligent Systems Reference Library}, year = {2017}, pages = {23{\textendash}48}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {Knowledge-Based Leisure Time Recommendations in Social Networks}, abstract = {We introduce a novel knowledge-based recommendation algorithm for leisure time information to be used in social networks, which enhances the state-of-the-art in this algorithm category by taking into account (a) qualitative aspects of the recommended places (restaurants, museums, tourist attractions etc.), such as price, service and atmosphere, (b) influencing factors between social network users, (c) the semantic and geographical distance between locations and (d) the semantic categorization of the places to be recommended. The combination of these features leads to more accurate and better user-targeted leisure time recommendations.}, keywords = {Attribute constraints, collaborative filtering, Knowledge-based Recommender Systems, Semantic Information, Social Networks}, doi = {10.1007/978-3-319-51905-0_2}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis}, editor = {Giner Alor Hernandez and Rafael Valencia Garcia} } @article {marg2017a-fgcs, title = {Query personalization using social network information and collaborative filtering techniques}, journal = {Future Generation of Computer Systems}, volume = {Special Issue on Recommender Systems for Large-Scale Social Networks}, year = {2017}, month = {jan}, pages = {440{\textendash}450}, abstract = {Query personalization has emerged as a means to handle the issue of information volume growth, aiming to tailor query answer results to match the goals and interests of each user. Query personalization dynamically enhances queries, based on information regarding user preferences or other contextual information; typically enhancements relate to incorporation of conditions that filter out results that are deemed of low value to the user and/or ordering results so that data of high value are presented first. In the domain of personalization, social network information can prove valuable; users{\textquoteright} social networks profiles, including their interests, influence from social friends, etc. can be exploited to personalize queries. In this paper, we present a query personalization algorithm, which employs collaborative filtering techniques and takes into account influence factors between social network users, leading to personalized results that are better-targeted to the user.}, doi = {10.1016/j.future.2017.03.015}, url = {http://www.sciencedirect.com/science/article/pii/S0167739X17303692}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @article {margaris-csimq-2017, title = {Using Time Clusters for Following Users{\textquoteright} Shifts in Rating Practices}, journal = {Complex Systems Informatics and Modeling Quarterly}, volume = {13}, year = {2017}, abstract = {Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user{\textquoteright}s rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users{\textquoteright} ratings is inadequate, since it fails to follow such shifts in users{\textquoteright} rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in user-user collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users{\textquoteright} rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.}, keywords = {collaborative filtering, Dynamic Average, Rating Abstention Interval, Rating Time Clusters, Ratings{\textquoteright} Timestamps, Recommender Systems}, issn = {2255-9922}, doi = {10.7250/csimq.2017-13.02}, author = {Dionisis Margaris and Costas Vassilakis} } @conference {margaris2016a, title = {Improving QoS Delivered by WS-BPEL Scenario Adaptation through Service Execution Parallelization}, booktitle = {Proceedings of the 31st Annual ACM Symposium on Applied Computing}, year = {2016}, pages = {1590-1596}, publisher = {ACM}, organization = {ACM}, abstract = {WS-BPEL scenario execution adaptation has been proposed by researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences; these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we study the potential to parallelize the execution of the WS-BPEL scenario in order to minimize its response time and/or achieving higher scores in the other qualitative dimensions, such as cost, reliability, etc., at the same time. We also describe, develop and validate a parallelization algorithm for realizing the proposed enhancements.}, doi = {10.1145/2851613.2851805}, url = {https://dl.acm.org/citation.cfm?id=2851805}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {marg2016-ssci, title = {Pruning and Aging for User Histories in Collaborative Filtering}, booktitle = {Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence}, year = {2016}, month = {12}, abstract = {In this paper, we introduce algorithms for pruning and aging user ratings in collaborative filtering systems, based on their oldness, under the rationale that aged user ratings may not accurately reflect the current state of users regarding their preferences. The aging algorithm reduces the importance of aged ratings, while the pruning algorithm removes them from the database. The algorithms are evaluated against various types of datasets. The pruning algorithm has been found to present a number of advantages, namely (1) reducing the rating database size, (2) achieving better prediction generation times and (3) improving prediction quality by cutting off predictions with high error. The algorithm can be used in all rating databases that include a timestamp and has been proved to be effective in any type of dataset, from movies and music, to videogames and books.}, doi = {10.1109/ssci.2016.7849920}, url = {https://ieeexplore.ieee.org/document/7849920/}, author = {Dionisis Margaris and Costas Vassilakis} } @article {Margaris_2016, title = {Recommendation information diffusion in social networks considering user influence and semantics}, journal = {Social Network Analysis and Mining}, volume = {6}, number = {1}, year = {2016}, month = {nov}, pages = {108}, publisher = {Springer Nature}, abstract = {One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users{\textquoteright} profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items{\textquoteright} general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore online advertisement efficiency is raised.}, keywords = {Collaborative filtering, Information diffusion, Quality of service, Semantic Information, Social Networks}, doi = {10.1007/s13278-016-0416-z}, url = {http://rdcu.be/mZ7e}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {marg15a, title = {A Collaborative Filtering Algorithm with Clustering for Personalized Web Service Selection in Business Processes}, booktitle = {Proceedings of RCIS 2015}, year = {2015}, month = {5}, abstract = {Recommender systems aim to propose items that are expected to be of interest to the users. As one of the most successful approaches to building recommender systems, collaborative filtering exploits the known preferences of a group of users to formulate recommendations or predictions of the unknown preferences for other users. In many cases, collaborative filtering algorithms handle complex items, which are described using hierarchical tree structures containing rich semantic information. In order to make accurate recommendations on such items, the related algorithms must examine all aspects of the available semantic information. Thus, when collaborative filtering techniques are employed to adapt the execution of business processes, they must take into account the services{\textquoteright} Quality of Service parameters, so as to generate recommendations tailored to the individual user needs. In this paper, we present a collaborative filtering-based algorithm which takes into account the web services{\textquoteright} QoS parameters in order to tailor the execution of business processes to the preferences of users. An offline clustering technique is also introduced for supporting the efficient and scalable execution of proposed algorithm under the presence of large repositories of sparse data.}, keywords = {business processes, clustering, collaborative filtering, hierarchical tree, performance, Quality of service, Web services}, doi = {10.1109/RCIS.2015.7128877}, author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis} } @conference {margaris2015a, title = {A Hybrid Framework for WS-BPEL Scenario Execution Adaptation, Using Monitoring and Feedback Data}, booktitle = {Proceedings of the 30th ACM/SIGAPP Symposium On Applied Computing, SOAP track}, year = {2015}, month = {4}, publisher = {ACM}, organization = {ACM}, abstract = {In this paper, we present a framework which provides runtime adaptation for BPEL scenarios. The adaptation is based on (a) quality of service parameters of available web services (b) quality of service policies specified by users (c) collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, (d) monitoring, in order to follow the variations of QoS attribute values and (e) on users{\textquoteright} opinions services they have used.}, doi = {10.1145/2695664.2695687}, url = {https://dl.acm.org/citation.cfm?doid=2695664.2695687}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @article {margaris14a, title = {An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques}, journal = {Science of Computer Programming}, volume = {98}, year = {2015}, pages = {707{\textendash}734}, abstract = {In this paper, we present a framework which incorporates runtime adaptation for BPEL scenarios. The adaptation is based on (a) the quality of service parameters of available services, allowing for tailoring their execution to the diverse needs of individual users and (b)collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes. The proposed framework also caters maintaining the transactional semantics that invocations to multiple services offered by the same provider may bear.}, doi = {10.1016/j.scico.2014.10.007}, url = {http://www.sciencedirect.com/science/article/pii/S0167642314004778}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} } @conference {margaris15c, title = {On Replacement Service Selection in WS-BPEL Scenario Adaptation}, booktitle = {Proceedings of the 8th IEEE International Conference on Service Oriented Computing \& Applications (SOCA 2015)}, year = {2015}, month = {9}, publisher = {IEEE}, organization = {IEEE}, abstract = {WS-BPEL scenario execution adaptation has been proposed by numerous researchers as a response to the need of users to tailor the WS-BPEL scenario execution to their individual preferences; these preferences are typically expressed through Quality of Service (QoS) policies, which the adaptation mechanism considers in order to select the services that will ultimately be invoked to realize the desired business process. In this paper, we consider a number of issues related to WS-BPEL scenario adaptation, aiming to enhance adaptation quality and improve the QoS offered to end users. More specifically, with the goal of broadening the service selection pool we (a) discuss the identification of potential services that can be used to realize a functionality used in the WS-BPEL scenario and (b) elaborate on transactional semantics that invocations to multiple services offered by the same provider may bear. We also describe and validate an architecture for realizing the proposed enhancements.}, doi = {10.1109/SOCA.2015.11}, author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis} } @conference {Margaris2013RCIS, title = {Adapting WS-BPEL scenario execution using collaborative filtering techniques}, booktitle = {IEEE 7th International Conference on Research Challenges in Information Science (RCIS)}, year = {2013}, month = {may}, publisher = {IEEE}, organization = {IEEE}, abstract = {WS-BPEL has been adopted as the predominant method for composing individual web services into higher-level business processes. The designers of WS-BPEL scenarios define at development time the specific web services that will be invoked in the context of the business process they model; in the context however of the current web, where each functionality is offered by multiple service providers, under different quality of service parameters, using a fixed BPEL scenario has been recognized to be inadequate for servicing the diverse needs of business processes clients. To this end, WS-BPEL scenario execution adaptation has been proposed, mainly allowing clients to specify quality of service policies, which drive the dynamic selection of the services that will be invoked. In this paper, we present a framework extending the quality of service-based adaptation mechanisms with collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes.}, doi = {10.1109/rcis.2013.6577691}, author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis} } @conference {Margaris2013SAC, title = {An integrated framework for QoS-based adaptation and exception resolution in WS-BPEL scenarios}, booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC \textquotesingle13}, year = {2013}, month = {3}, publisher = {ACM Press}, organization = {ACM Press}, abstract = {In this paper, we present a framework which incorporates runtime quality of service-based adaptation for BPEL scenarios, allowing for tailoring their execution to the diverse needs of individual users. The proposed framework also caters for automatically resolving system-level exceptions, such as machine outages or network partitionings, while both scenario execution adaptation and exception resolution maintain the transactional semantics that invocations to multiple services offered by the same provider may bear.}, doi = {10.1145/2480362.2480714}, author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis} }