LsRec: Large-scale social recommendation with online update. (30th December 2020)

• Record Type: Journal Article
• Authors: Zhou, Wang; Zhou, Yongluan; Li, Jianping; Memon, Muhammad Hammad
• Is Part Of: Expert systems with applications. Volume 162(2020)
• Publication Date: 2020-12-30
• Subjects: Social recommendation -- Online update -- Item clustering -- Matrix factorization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113739
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Social influence could capture the intricate relations between users. Item clustering could reflect user's preference. Recommendation within each item cluster achieves high performance. The incremental update strategy guarantees flexible online scalability. Computational cost decreases with online incremental update. Abstract: With the ever-increasing scale and complexity of social network and online business, Recommender Systems (RS) have played crucial roles in information processing and filtering in various online applications, although suffering from such as data sparsity and low accuracy problems. Meanwhile, recent researches try to enhance the performance of RS through such social network and clustering algorithms, however, they may fail to achieve further improvement in large-scale online recommendation due to the serious information overload. In this article, a novel social recommendation approach with online update referred to as LsRec is proposed, which generally contains offline computation and online incremental update. More precisely, LsRec not only takes account of user's social relationship, but also clusters items according to the similarity degree, furthermore, LsRec performs recommendation in each generated cluster respectively. In practice, LsRec could be capable of exploiting user-level social influence, and capturing the intricate relationship between items. In addition, theoretical proof could provide convergence guarantee for the model. Specifically, with the appealing merit of flexible online update scenario, LsRec could yield high performance in large-scale online recommendation with low computational complexity. Extensive experimental analysis over four real world datasets demonstrate the effectiveness and efficiency of LsRec, which indicates that LsRec could significantly outperform state-of-the-art recommender approaches, especially in large-scale online recommendation.