Abstract
This article presents a new approach to collaborative filtering recommender systems that focuses on the problem of an active user’s (a user to whom recommendations are generated) neighbourhood modelling. Precise identification of the neighbours has a direct impact on the quality of the generated recommendation lists. Clustering techniques are the solution that is often used for neighbourhood calculation, however, they negatively affect the quality (precision) of recommendations.
In this article, a new version of the algorithm based on multi-clustering, \(M-CCF\), is proposed. Instead of one clustering scheme, it works on a set of multi-clusters, therefore it selects the most appropriate one that models the neighbourhood most precisely. This article presents the results of the experiments validating the advantage of multi-clustering approach, \(M-CCF\), over the traditional methods based on single-scheme clustering. The experiments focus on the overall recommendation performance including accuracy and coverage as well as a cold-start problem.
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The work was supported by the grant from Bialystok University of Technology WZ/WI-IIT/2/2020 and funded with resources for research by the Ministry of Science and Higher Education in Poland.
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Kużelewska, U. (2021). Quality of Recommendations and Cold-Start Problem in Recommender Systems Based on Multi-clusters. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_6
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