@conference {873,
	title = {Optimizing Collaborative Filtering in Very Sparse Datasets: a Confidence-based Approach},
	booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications},
	year = {2026},
	publisher = {IEEE Xplore},
	organization = {IEEE Xplore},
	abstract = {<p>Collaborative filtering systems are a key tool for personalized recommendations, predicting user preferences based on historical data. A major challenge in collaborative filtering is the reliability of predictions, particularly in highly sparse datasets, where the limited overlap of common ratings between users impedes the identification of reliable neighbors, reducing both the accuracy and coverage of the predictions. So far, the literature has proposed confidence factors to quantify prediction reliability, typically using the Pearson and Cosine similarities with threshold-based neighbor selection. However, a recent study demonstrated that using the Sigmoid Cosine similarity metric in combination with the KNN neighborhood selection method and adapting the value of K to each level of dataset sparsity, significantly improves prediction performance. In this paper, we revisit the calculation of confidence factors under these improved settings, assessing their validity and effectiveness across datasets with varying sparsity levels. Continuous assessment of collaborative filtering methods and adjustment of their parameters according to dataset characteristics is particularly crucial in highly sparse datasets, where the reliability of neighbors strongly influences prediction quality.</p>
},
	keywords = {collaborative filtering, Confidence Factors, Optimization, Rating Predictions, Recommender Systems, Sparse Datasets},
	author = {Vassiliki Stouraiti and Giorgos Mpardis and Konstantinos I. Roumeliotis and Costas Vassilakis and Christos Tryfonopoulos and Dimitris Spiliotopoulos}
}
@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}
}
