2009 RegressionbasedLatentFactorMode

• (Agarwal et al., 2009) ⇒ Deepak Agarwal, and Bee-Chung Chen. (2009). “Regression-based Latent Factor Models.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557029

Subject Headings: Latent Factor Modeling.

Notes

• Presentation Video: http://videolectures.net/kdd09_agarwal_rblfm/

Cited By

• http://scholar.google.com/scholar?q=%22Regression-based+latent+factor+models%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557029&preflayout=flat#citedby

Quotes

Abstract

We propose a novel latent factor model to accurately predict response for large scale dyadic data in the presence of features. Our approach is based on a model that predicts response as a multiplicative function of row and column latent factors that are estimated through separate regressions on known row and column features. In fact, our model provides a single unified framework to address both cold and warm start scenarios that are commonplace in practical applications like recommender systems, online advertising, web search, etc. We provide scalable and accurate model fitting methods based on Iterated Conditional Mode and Monte Carlo EM algorithms. We show our model induces a stochastic process on the dyadic space with kernel (covariance) given by a polynomial function of features. Methods that generalize our procedure to estimate factors in an online fashion for dynamic applications are also considered. Our method is illustrated on benchmark datasets and a novel content recommendation application that arises in the context of Yahoo! Front Page. We report significant improvements over several commonly used methods on all datasets.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 RegressionbasedLatentFactorMode	Bee-Chung Chen Deepak Agarwal			Regression-based Latent Factor Models		KDD-2009 Proceedings		10.1145/1557019.1557029		2009