JUCS - Journal of Universal Computer Science 27(7): 667-692, doi: 10.3897/jucs.70341
BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks
expand article infoLamia Berkani, Lylia Betit, Louiza Belarif
‡ USTHB University, Algiers, Algeria
Open Access

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

Social recommendation, Collaborative filtering, Hybrid filtering, Clustering, Multiview clustering, Optimized-based clustering, Bees Swarm optimization algorithm, Optimized multiview clustering