文章基本信息

标题：BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks
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作者：Lamia Berkani ; Lylia Betit ; Louiza Belarif 等
期刊名称：Journal of Universal Computer Science
印刷版ISSN：0948-6968
出版年度：2021
卷号：27
期号：7
页码：667-692
DOI：10.3897/jucs.70341
语种：English
出版社：Graz University of Technology and Know-Center
摘要：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.