2012 LearningPersonalSocialLatentFac

• (Shen & Jin, 2012) ⇒ Yelong Shen, and Ruoming Jin. (2012). “Learning Personal + Social Latent Factor Model for Social Recommendation.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339732

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Learning+Personal+%2B+Social+Latent+Factor+Model+for+Social+Recommendation
• http://dl.acm.org/citation.cfm?id=2339530.2339732&preflayout=flat#citedby

Quotes

Author Keywords

• Information filtering; personal + social factor; social recommender system

Abstract

Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship.

In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 LearningPersonalSocialLatentFac	Ruoming Jin Yelong Shen			Learning Personal + Social Latent Factor Model for Social Recommendation				10.1145/2339530.2339732		2012