@article {797,
	title = {On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings},
	journal = {SN Computer Science},
	volume = {2},
	year = {2021},
	month = {May},
	pages = {255},
	abstract = {<p>Collaborative filtering-based recommendation systems consider users{\textquoteright} likings and interests, articulated as ratings within a database to offer personalized recommendations. Unfortunately, many collaborative filtering datasets exhibit the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} phenomenon, a state where no near neighbours can be found for certain users. This phenomenon is extremely frequent in datasets where users, on average, have rated only a small percentage of the available items, which are termed as sparse datasets. This paper addresses the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} problem by proposing the virtual ratings concept and introduces an algorithm for virtual rating creation on the basis of actual ratings. The novelty behind this concept is that the introduction of the virtual ratings effectively reduces the user{\textendash}item rating matrix sparsity, thus alleviating the aforementioned problem. The proposed algorithm, which is termed as CFVR, has been extensively evaluated and the results show that it achieves to considerably improve the capability of a collaborative filtering system to formulate tailored recommendations for each user, when operating on sparse datasets, while at the same time improves rating prediction quality.</p>
},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Recommender Systems, Sparse Datasets, Virtual ratings},
	issn = {2661-8907},
	doi = {10.1007/s42979-021-00668-8},
	url = {https://doi.org/10.1007/s42979-021-00668-8},
	author = {Margaris, Dionisis and Spiliotopoulos, Dimitris and Karagiorgos, Gregory and Vassilakis, Costas and Vasilopoulos, Dionysios}
}
@conference {802,
	title = {A User Interface for Personalising WS-BPEL Scenarios},
	booktitle = {Proceedings of the 2021 International Conference on Human-Computer Interaction: HCI in Business, Government and Organizations},
	year = {2021},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {<p>Due to the huge volume of web services available, both locally and in the cloud, the performance of users and systems need significant research attention. Since WS-BPEL is the dominant language for orchestrating individual web services into business processes, by composing WS-BPEL scripts/scenarios, graphical notations facilitating WS-BPEL design can be extremely useful. Current user interfaces allow WS-BPEL designers not only to invoke selected web services, but also to explicitly ask for recommendations. Then, the user interface appends the services achieving the highest score, according to the attributes{\textquoteright} importance, set by the designer, to their WS-BPEL scenario. However, since the final selection is produced automatically, rather than relying on the designers{\textquoteright} choices, many times, from a personalisation point of view, the adaptation fails. This work reports on the design, development and user evaluation of a user interface that incorporates functionalities that support the designers{\textquoteright} selection performance, thereby upgrading the personalisation level of the WS-BPEL scenarios, as well as the success of the adaptation.</p>
},
	keywords = {Personalisation, Recommender Systems, Usability, User evaluation, user interface, Web services, WS-BPEL scenarios},
	isbn = {978-3-030-77750-0},
	doi = {https://doi.org/10.1007/978-3-030-77750-0_25},
	author = {Margaris, Dionisis and Spiliotopoulos, Dimitris and Vasilopoulos, Dionysios and Vassilakis, Costas}
}
