@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}
}
@article {820,
	title = {Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets},
	journal = {Big Data and Cognitive Computing},
	volume = {7},
	year = {2023},
	month = {mar},
	pages = {59},
	abstract = {<p>Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered {\textquotedblleft}close{\textquotedblright} to the user for whom the prediction is being generated have given to the same product or service. In general, {\textquotedblleft}close{\textquotedblright} users for some user u correspond to users that have rated items similarly to u and these users are termed as {\textquotedblleft}near neighbors{\textquotedblright}. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset{\textquoteright}s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method{\textquoteright}s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.</p>
},
	keywords = {collaborative filtering, Evaluation, low density datasets, personalization, rating prediction quality, Recommender Systems, reliability},
	doi = {10.3390/bdcc7020059},
	url = {https://www.mdpi.com/2504-2289/7/2/59},
	author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos and Stefanos Ougiaroglou}
}
