@article {854,
	title = {Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality},
	journal = {Technologies},
	volume = {13},
	year = {2025},
	pages = {181},
	abstract = {<p>Recommender systems suggest items that users are likely to accept by predicting ratings for items they have not already rated. Collaborative filtering is a widely used method that produces these predictions, based on the ratings of similar users, termed as near neighbors. However, in many cases, prediction errors occur and, therefore, the recommender system ends up either recommending unwanted products or missing out on products the user would actually desire. As a result, the quality of the recommendations that are produced is of major importance. In this paper, we introduce an advanced collaborative filtering recommendation algorithm that upgrades the quality of the recommendations that are produced by considering, along with the rating prediction value of the items computed by the plain collaborative filtering procedure, a number of confidence factors that each rating prediction fulfills. The presented algorithm maintains high recommendation coverage, and can be applied to every collaborative filtering dataset, since it is based only on the very basic information. Based on the application of the algorithm on widely used recommender systems datasets, the proposed algorithm significantly upgrades the recommendation quality, surpassing the performance of state-of-the-art research works that also consider confidence factors.</p>
},
	keywords = {Algorithm, collaborative filtering, prediction confidence factors, recommendation quality, Recommender Systems},
	issn = {2227-7080},
	doi = {https://doi.org/10.3390/technologies13050181},
	url = {https://doi.org/10.3390/technologies13050181},
	author = {Dionisis Margaris and Spiliotopoulos, Dimitris and Sgardelis, Kiriakos and Costas Vassilakis}
}
@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}
}
