@article {860,
	title = {An evaluation review of user similarity metrics in sparse collaborative filtering datasets},
	journal = {International Journal of Data Science and Analytics},
	year = {2025},
	abstract = {<p>Collaborative filtering (CF) is one of the most prominent recommender system (RecSys) techniques of the recent years. CF generates rating predictions for the items that the user has not evaluated yet, using the evaluations of users with similar likings to the same items. Therefore, in CF the task of finding these users (which can be considered as reliable recommenders) is of high importance, while this task is especially challenging on sparse datasets. To this end, many user similarity metrics have been introduced and used in the literature, such as the Vector (or Cosine) Similarity metric, the Spearman rank correlation, the Pearson Correlation Coefficient (PCC), and others. For a CF RecSys, the use of the most efficient similarity metric is of great importance. This paper assesses the effectiveness of 15 user similarity metrics in sparse CF datasets, by conducting an extensive set of experiments. These experiments include 10 sparse CF datasets with diverse item domains, two neighbour selection approaches, two rating prediction formulas, and three rating prediction accuracy metrics. The evaluation results show that the metrics that achieve the best prediction results are found to be the Spearman rank correlation, followed by the Adjusted Rand Index, the Constrained PCC, and the Chebysev distance. Interestingly, the most widely used similarity metrics in CF research, i.e. the PCC and the Cosine Similarity, are not among the best performing metrics.</p>
},
	keywords = {collaborative filtering, Evaluation review, Recommender Systems, Sparse Datasets, User similarity metrics},
	issn = {2364-4168},
	doi = {10.1007/s41060-025-00846-4},
	author = {Sgardelis, Kiriakos and Margaris, Dionisis 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 = {Dionisis Margaris and Spiliotopoulos, Dimitris and Karagiorgos, Gregory and Costas Vassilakis and Vasilopoulos, Dionysios}
}
