Recommender systems using cluster-indexing collaborative filtering and social data analytics

Kyoung-jae Kim and Hyunchul Ahn

International Journal of Production Research, 2017, vol. 55, issue 17, 5037-5049

Abstract: As a result of the extensive variety of products available in e-commerce settings during the last decade, recommender systems have been highlighted as a means of mitigating the problem of information overload. Collaborative filtering (CF) is the most widely used algorithm to build such systems, and improving the predictive accuracy of CF-based recommender systems has been a major research challenge. This research aims to improve the prediction accuracy of CF by incorporating social network analysis (SNA) and clustering techniques. Our proposed model identifies the most influential people in an online social network by SNA and then conducts clustering analysis using these people as initial centroids (cluster centres). Finally, the model makes recommendations using cluster-indexing CF based on the clustering outcomes. In this step, our model adjusts the effect of neighbours in the same cluster as the target user to improve prediction accuracy by reflecting hidden information about his or her social community. The experimental results indicate that the proposed model outperforms other comparison models, including conventional CF, with statistical significance.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2017.1287443 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:55:y:2017:i:17:p:5037-5049

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2017.1287443

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().