2014 LeveragingUserLibrariestoBootst

• (Charlin et al., 2014) ⇒ Laurent Charlin, Richard S. Zemel, and Hugo Larochelle. (2014). “Leveraging User Libraries to Bootstrap Collaborative Filtering.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623663

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Leveraging+User+Libraries+to+Bootstrap+Collaborative+Filtering
• http://dl.acm.org/citation.cfm?id=2623330.2623663&preflayout=flat#citedby

Quotes

Author Keywords

• Cold start; collaborative filtering; document recommendations; information filtering; parameter learning; side information; topic modeling

Abstract

We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM's chief novelty lies in its learned model of individual libraries, or sets of documents, associated with each user. Overall, CSTM is a joint directed probabilistic model of user-item scores (ratings), and the textual side information in the user libraries and the items. Creating a generative description of scores and the text allows CSTM to perform well in a wide variety of data regimes, smoothly combining the side information with observed ratings as the number of ratings available for a given user ranges from none to many. Experiments on real-world datasets demonstrate CSTM's performance. We further demonstrate its utility in an application for personal recommendations of posters which we deployed at the NIPS 2013 conference.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 LeveragingUserLibrariestoBootst	Hugo Larochelle Laurent Charlin Richard S. Zemel			Leveraging User Libraries to Bootstrap Collaborative Filtering				10.1145/2623330.2623663		2014