Collaborative matrix factorization mechanism for group recommendation in big data-based library systems
ISSN: 0737-8831
Article publication date: 5 February 2018
Issue publication date: 4 June 2018
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.
Keywords
Acknowledgements
This work is supported by the Major Program of the National Natural Science Foundation of China (71490725), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (71521001), the National Natural Science Foundation of China (71501057, 71722010, 91546114, 71371062) and the National Key Technology Support Program (2015BAH26F00).
Citation
Liu, Y., Yang, L., Sun, J., Jiang, Y. and Wang, J. (2018), "Collaborative matrix factorization mechanism for group recommendation in big data-based library systems", Library Hi Tech, Vol. 36 No. 3, pp. 458-481. https://doi.org/10.1108/LHT-06-2017-0121
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited