The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Exploring social influence for recommendation: a generative model approach
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.
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Cites
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Maximizing the spread of influence through a social network
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Using collaborative filtering to weave an information tapestry
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Compliance, identification, and internalization three processes of attitude change
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Scalable influence maximization for prevalent viral marketing in large-scale social networks
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Cited by
Influence Maximization on Social Graphs: A Survey
Li, Y., Fan, J., Wang, Y., & Tan, K.-L. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807843 · Cited by 659
Influence analysis in social networks: A survey
Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.01.005 · Cited by 308
Social influence modeling using information theory in mobile social networks
Peng, S., Yang, A., Cao, L., Yu, S., & Xie, D. (2017) · Information Sciences · doi:10.1016/j.ins.2016.08.023 · Cited by 200
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
Chaney, A. J. B., Blei, D. M., & Eliassi-Rad, T. (2015) · Proceedings of the 9th ACM Conference on Recommender Systems · doi:10.1145/2792838.2800193 · Cited by 164
Learning Personalized Preference of Strong and Weak Ties for Social Recommendation
Wang, X., Hoi, S. C. H., Ester, M., Bu, J., & Chen, C. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052556 · Cited by 97
Metadata
- Authors
- Ye, M., Liu, X., & Lee, W.-C.
- Venue
- Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2012-08-12
- Pages
- 671-680
- OpenAlex
- W2080567541
- Cited by
- 288
- References
- 27
- FWCI
- 17.18
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ye, M., Liu, X., & Lee, W.-C. (2012). Exploring social influence for recommendation: a generative model approach. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/2348283.2348373
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