Exploring social influence for recommendation - A generative model approach

Mao Ye, Xingjie Liu, Wang Chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

176 Scopus citations

Abstract

Social friendship has been shown beneficial for item recommendation for years. However, existing approaches mostly incorporate social friendship into recommender systems by heuristics. In this paper, we argue that social influence between friends can be captured quantitatively and propose a probabilistic generative model, called social influenced selection(SIS), to model the decision making of item selection (e.g., what book to buy or where to dine). Based on SIS, we mine the social influence between linked friends and the personal preferences of users through statistical inference. To address the challenges arising from multiple layers of hidden factors in SIS, we develop a new parameter learning algorithm based on expectation maximization (EM). Moreover, we show that the mined social influence and user preferences are valuable for group recommendation and viral marketing. Finally, we conduct a comprehensive performance evaluation using real datasets crawled from last.fm and whrrl.com to validate our proposal. Experimental results show that social influence captured based on our SIS model is effective for enhancing both item recommendation and group recommendation, essential for viral marketing, and useful for various user analysis.

Original languageEnglish (US)
Title of host publicationSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages671-680
Number of pages10
DOIs
StatePublished - Sep 28 2012
Event35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 - Portland, OR, United States
Duration: Aug 12 2012Aug 16 2012

Publication series

NameSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012
CountryUnited States
CityPortland, OR
Period8/12/128/16/12

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All Science Journal Classification (ASJC) codes

  • Information Systems

Cite this

Ye, M., Liu, X., & Lee, W. C. (2012). Exploring social influence for recommendation - A generative model approach. In SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 671-680). (SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/2348283.2348373