Authors:
Kiriakos Sgardelis
1
;
Dionisis Margaris
1
;
Dimitris Spiliotopoulos
2
;
Costas Vassilakis
3
and
Stefanos Ougiaroglou
4
Affiliations:
1
Department of Digital Systems, University of the Peloponnese, Sparta, Greece
;
2
Department of Management Science and Technology, University of the Peloponnese, Tripoli, Greece
;
3
Department of Informatics and Telecommunications, University of the Peloponnese, Tripoli, Greece
;
4
Department of Information and Electronic Engineering, International Hellenic University, Thessaloniki, Greece
Keyword(s):
Collaborative Filtering, Rating Predictions, Recommender Systems, Certainty Factors, Algorithm.
Abstract:
Collaborative filtering is a prevalent recommender system technique which generates rating predictions based on the rating values given by the users’ 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 confidenc
e 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.
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