@article {841,
	title = {Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering},
	journal = {Big Data and Cognitive Computing},
	volume = {8},
	year = {2024},
	pages = {53},
	abstract = {<p>Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the user. However, as expected, prediction estimation may contain errors, which, in the case of RecSys, will lead to either not recommending a product that the user would actually like (i.e., purchase, watch, or listen) or to recommending a product that the user would not like, with both cases leading to degraded recommendation quality. Especially in the latter case, the RecSys would be deemed unreliable. In this work, we design and develop a recommendation algorithm that considers both the rating prediction values and the prediction confidence, derived from features associated with rating prediction accuracy in collaborative filtering. The presented algorithm is based on the rationale that it is preferable to recommend an item with a slightly lower prediction value, if that prediction seems to be certain and safe, over another that has a higher value but of lower certainty. The proposed algorithm prevents low-confidence rating predictions from being included in recommendations, ensuring the recommendation quality and reliability of the RecSys.</p>
},
	keywords = {Algorithm, collaborative filtering, confidence, Evaluation, Rating Predictions, Recommender Systems, Uncertainty},
	issn = {2504-2289},
	doi = {10.3390/bdcc8060053},
	author = {Margaris, Dionisis and Sgardelis, Kiriakos and Spiliotopoulos, Dimitris and Vassilakis, Costas}
}
@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}
}
