Collaborative matrix factorization mechanism for group recommendation in big data-based library systems. Issue 3 (5th February 2018)

• Record Type: Journal Article
• Authors: Liu, Yezheng; Yang, Lu; Sun, Jianshan; Jiang, Yuanchun; Wang, Jinkun
• Is Part Of: Library hi tech. Volume 36:Issue 3(2018)
• Page Start: 458
• Page End: 481
• Publication Date: 2018-02-05
• Subjects: Big data analytics -- Collaborative matrix factorization -- Group recommendation -- Online library system -- Personalized services -- Scientific article recommendation; Library science -- Technological innovations -- Periodicals; Libraries -- Automation -- Periodicals; Information science -- Periodicals; 025.00285
• DOI: 10.1108/LHT-06-2017-0121
• Languages: English
• ISSNs: 0737-8831

Abstract

Abstract : Purpose: Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars' research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups. Design/methodology/approach: The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed. Findings: Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment. Research limitations/implications: The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts. Practical implications: The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient. Social implications: The proposed methods have potential value to improve scientific collaboration and research innovation. Originality/value: The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.