2013 MakingRecommendationsfromMultip

• (Chen et al., 2013) ⇒ Wei Chen, Wynne Hsu, and Mong Li Lee. (2013). “Making Recommendations from Multiple Domains.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487638

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Making+Recommendations+from+Multiple+Domains
• http://dl.acm.org/citation.cfm?id=2487575.2487638&preflayout=flat#citedby

Quotes

Author Keywords

• Collaborative filtering; information filtering; personalization; recommendation; social trust

Abstract

Given the vast amount of information on the World Wide Web, recommender systems are increasingly being used to help filter irrelevant data and suggest information that would interest users. Traditional systems make recommendations based on a single domain e.g., movie or book domain. Recent work has examined the correlations in different domains and designed models that exploit user preferences on a source domain to predict user preferences on a target domain. However, these methods are based on matrix factorization and can only be applied to two-dimensional data. Transferring high dimensional data from one domain to another requires decomposing the high dimensional data to binary relations which results in information loss.

Furthermore, this decomposition creates a large number of matrices that need to be transferred and combining them in the target domain is non-trivial. Separately, researchers have looked into using social network information to improve recommendation. However, this social network information has not been explored in cross domain collaborative filtering. In this work, we propose a generalized cross domain collaborative filtering framework that integrates social network information seamlessly with cross domain data. This is achieved by utilizing tensor factorization with topic based social regularization. This framework is able to transfer high dimensional data without the need for decomposition by finding shared implicit cluster-level tensor from multiple domains. Extensive experiments conducted on real world datasets indicate that the proposed framework outperforms state-of-art algorithms for item recommendation, user recommendation and tag recommendation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 MakingRecommendationsfromMultip	Wei Chen Wynne Hsu Mong Li Lee			Making Recommendations from Multiple Domains				10.1145/2487575.2487638		2013