@article {852,
	title = {From Rating Predictions to Reliable Recommendations in Collaborative Filtering: The Concept of Recommendation Reliability Classes},
	journal = {Big Data and Cognitive Computing},
	volume = {9},
	year = {2025},
	pages = {106},
	abstract = {<p>Recommender systems aspire to provide users with recommendations that have a high probability to be accepted. This is accomplished by producing rating predictions for products that they have not evaluated and afterwards, the products having the highest prediction scores are recommended to them. Collaborative filtering is a popular recommender system technique which generates rating prediction scores by blending the ratings that users with similar likings have previously set to these products. However, predictions may entail errors, which will either lead to recommending products that the users would not accept or failing to recommend products that the users would really accept. The first case is considered much more critical, since the recommender system will significantly lose reliability and consequently interest. In this paper, after performing a study on rating prediction confidence factors in collaborative filtering (a) we introduce the concept of prediction reliability classes, (b) we rank these classes in relation to the utility of the rating predictions belonging to each class and (c) we present a collaborative filtering recommendation algorithm which exploits these reliability classes for prediction formulation. The efficacy of the presented algorithm is evaluated through an extensive multi-parameter evaluation process, which demonstrates that it significantly enhances recommendation quality.</p>
},
	keywords = {collaborative filtering, Evaluation, Rating Predictions, Recommender Systems, reliability classes, reliable recommendations},
	issn = {2504-2289},
	doi = {10.3390/bdcc9040106},
	author = {Dionisis Margaris and Costas Vassilakis and Spiliotopoulos, Dimitris}
}
