Faster methods for random sampling
Communications of the ACM
Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Machine Learning
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
IEEE Transactions on Knowledge and Data Engineering
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Exploratory Under-Sampling for Class-Imbalance Learning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Improving one-class collaborative filtering by incorporating rich user information
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Unifying explicit and implicit feedback for collaborative filtering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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
Multi-value probabilistic matrix factorization for IP-TV recommendations
Proceedings of the fifth ACM conference on Recommender systems
Adaptive social similarities for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
User graph regularized pairwise matrix factorization for item recommendation
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Expectation-Maximization collaborative filtering with explicit and implicit feedback
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Climbing the app wall: enabling mobile app discovery through context-aware recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
Social temporal collaborative ranking for context aware movie 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
A hidden Markov model for collaborative filtering
MIS Quarterly
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
One-class collaborative filtering with random graphs
Proceedings of the 22nd international conference on World Wide Web
Pairwise learning in recommendation: experiments with community recommendation on linkedin
Proceedings of the 7th ACM conference on Recommender systems
Understanding and promoting micro-finance activities in Kiva.org
Proceedings of the 7th ACM international conference on Web search and data mining
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
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One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, classes are highly imbalanced, and the vast majority of data points are missing. The idea of introducing weights for missing parts of a matrix has recently been shown to help in OCCF. While existing weighting approaches mitigate the first two problems above, a sparsity preserving solution that would allow to efficiently utilize data sets with e.g., hundred thousands of users and items has not yet been reported. In this paper, we study three different collaborative filtering frameworks: Low-rank matrix approximation, probabilistic latent semantic analysis, and maximum-margin matrix factorization. We propose two novel algorithms for large-scale OCCF that allow to weight the unknowns. Our experimental results demonstrate their effectiveness and efficiency on different problems, including the Netflix Prize data.