@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}
}
@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}
}
