@conference {839,
	title = {Improving Recommendation Quality in Collaborative Filtering by Including Prediction Confidence Factors},
	booktitle = {Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 24)},
	year = {2024},
	publisher = {SCITEPRESS Digital Library},
	organization = {SCITEPRESS Digital Library},
	abstract = {<p>Collaborative filtering is a prevalent recommender system technique which generates rating predictions based on the rating values given by the users{\textquoteright} near neighbours. Consequently, for each user, the items scoring the highest prediction values are recommended to them. Unfortunately, predictions inherently entail errors, which, in the case of recommender systems, manifest as unsuccessful recommendations. However, along with each rating prediction value, prediction confidence factors can be computed. As a result, items having low prediction confidence factor values, can be either declined for recommendation or have their recommendation priority demoted. In the former case, some users may receive fewer recommended items or even none, especially when using a sparse dataset. In this paper, we present an algorithm that determines the items to be recommended by considering both the rating prediction values and confidence factors of predictions, allowing for predictions with higher confidence factors to outrank predictions with higher value, but lower confidence. The presented algorithm achieves to enhance the recommendation quality, while at the same time retaining the number of recommendations for each user.</p>
},
	keywords = {Algorithm, Certainty factors, collaborative filtering, Rating Predictions, Recommender Systems},
	author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Stefanos Ougiaroglou}
}
