2011 LocalizedFactorModelsforMultiCo

• (Agarwal et al., 2011) ⇒ Deepak Agarwal, Bee-Chung Chen, and Bo Long. (2011). “Localized Factor Models for Multi-context Recommendation.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020504

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• http://scholar.google.com/scholar?q=%222011%22+Localized+Factor+Models+for+Multi-context+Recommendation
• http://dl.acm.org/citation.cfm?id=2020408.2020504&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; contingency table analysis; correlation and regression analysis; data fusion; design; epinions; experimentation; kalman filter; meta-analysis; multivariate statistics; recommender systems

Abstract

Combining correlated information from multiple contexts can significantly improve predictive accuracy in recommender problems. Such information from multiple contexts is often available in the form of several incomplete matrices spanning a set of entities like users, items, features, and so on. Existing methods simultaneously factorize these matrices by sharing a single set of factors for entities across all contexts. We show that such a strategy may introduce significant bias in estimates and propose a new model that ameliorates this issue by positing local, context-specific factors for entities. To avoid over-fitting in contexts with sparse data, the local factors are connected through a shared global model. This sharing of parameters allows information to flow across contexts through multivariate regressions among local factors, instead of enforcing exactly the same factors for an entity, everywhere. Model fitting is done in an EM framework, we show that the E-step can be fitted through a fast multi-resolution Kalman filter algorithm that ensures scalability. Experiments on benchmark and real-world Yahoo! datasets clearly illustrate the usefulness of our approach. Our model significantly improves predictive accuracy, especially in cold-start scenarios.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 LocalizedFactorModelsforMultiCo	Bee-Chung Chen Deepak Agarwal Bo Long			Localized Factor Models for Multi-context Recommendation				10.1145/2020408.2020504		2011