Integrating content information with factorization-based collaborative filtering

Question

I'm reading some papers in CF and noticed that most state-of-the-art methods are based on different factorization methods on the rating matrix only. I'd like to know if there are some representative works on combining content information (e.g. user features and item features) into factorization. Any ideas?

Answer 1

I am a researcher in the field of recommender systems, and did some work on exactly that. Here are some papers on that topic:

1. Aditya Krishna Menon, Charles Elkan: A log-linear model with latent features for dyadic prediction, ICDM 2010
2. David Stern, Ralf Herbrich, and Thore Graepel: Matchbox: Large Scale Bayesian Recommendations, WWW 2009
3. Chong Wang, David Blei: Collaborative topic modeling for recommending scientific articles, KDD 2011
4. Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme: Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, ICDM 2010
5. D. Agarwal and B.-C. Chen. Regression-based latent factor models, KDD 2009
6. D. Agarwal and B.-C. Chen. fLDA: Matrix factorization through latent dirichlet allocation, WSDM 2010

Please note that (4) is a paper by me, so this is also some kind of advertisement ;-)

Also, the KDD Cup 2011 involved an item taxonomy, and there has been some interesting work on combining such taxonomy information with latent factor models at the workshop: http://kddcup.yahoo.com/workshop.php

Answer 2

See for example "5. Hybrid Collaborative Filtering Techniques" in

X. Su, T. M. Khoshgoftaar, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence (2009). PDF