Fab: content-based, collaborative recommendation
Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Computational advertising and recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Non-linear matrix factorization with Gaussian processes
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Overcoming browser cookie churn with clustering
Proceedings of the fifth ACM international conference on Web search and data mining
Preference relation based matrix factorization for recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
Proceedings of the sixth ACM international conference on Web search and data mining
HeteroMF: recommendation in heterogeneous information networks using context dependent factor models
Proceedings of the 22nd international conference on World Wide Web
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Predicting user "ratings" on items is a crucial task in recommender systems. Matrix factorization methods that computes a low-rank approximation of the incomplete user-item rating matrix provide state-of-the-art performance, especially for users and items with several past ratings (warm starts). However, it is a challenge to generalize such methods to users and items with few or no past ratings (cold starts). Prior work [4][32] have generalized matrix factorization to include both user and item features for performing better regularization of factors as well as provide a model for smooth transition from cold starts to warm starts. However, the features were incorporated via linear regression on factor estimates. In this paper, we generalize this process to allow for arbitrary regression models like decision trees, boosting, LASSO, etc. The key advantage of our approach is the ease of computing --- any new regression procedure can be incorporated by "plugging" in a standard regression routine into a few intermediate steps of our model fitting procedure. With this flexibility, one can leverage a large body of work on regression modeling, variable selection, and model interpretation. We demonstrate the usefulness of this generalization using the MovieLens and Yahoo! Buzz datasets.