Abstract
The review of the literature shows that the clustering-based approaches suffer from relatively low accuracy and coverage. We propose in this article a multi-views clustering approach for the recommendation of items in social networks. First, users are iteratively clustered from the views of both rating patterns and social information which includes different features, namely: friendship, trust and influence. Different clustering algorithms have been used (Kmedoids and CLARANS algorithms). Then, based on the multi-view clustering, recommendations are generated according to different hybridization. In order to evaluate our approach, experiments have been conducted using the well-known FilmTrust dataset. The results we have obtained show that our approach outperforms the existing related work approaches and baselines.
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Berkani, L., Betit, L., Belarif, L. (2021). A Multi-view Clustering Approach for the Recommendation of Items in Social Networking Context. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds) Advances in Computing Systems and Applications. CSA 2020. Lecture Notes in Networks and Systems, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-69418-0_22
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