Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Collaborative ordinal regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Major components of the gravity recommendation system
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Improving Maximum Margin Matrix Factorization
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Maximum margin matrix factorization for code recommendation
Proceedings of the third ACM conference on Recommender systems
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Proceedings of the fourth ACM conference on Recommender systems
Predicting most rated items in Weekly Recommendation with temporal regression
Proceedings of the Workshop on Context-Aware Movie Recommendation
Limitations of matrix completion via trace norm minimization
ACM SIGKDD Explorations Newsletter
A case study in a recommender system based on purchase data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Collaborative temporal order modeling
Proceedings of the fifth ACM conference on Recommender systems
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
SIAM Journal on Optimization
Mining contextual movie similarity with matrix factorization for context-aware recommendation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation
Information Sciences: an International Journal
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Future Generation Computer Systems
Retargeted matrix factorization for collaborative filtering
Proceedings of the 7th ACM conference on Recommender systems
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Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses. In this paper we discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph. We show equivalence between graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov (Advances in Neural Information Processing Systems 20, 2008). Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation.