@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 {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 = {Dionisis Margaris and Sgardelis, Kiriakos and Spiliotopoulos, Dimitris and Costas Vassilakis}
}
