Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
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
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
WSABIE: scaling up to large vocabulary image annotation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Supercharging recommender systems using taxonomies for learning user purchase behavior
Proceedings of the VLDB Endowment
Learning to rank social update streams
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
Latent factor models with additive and hierarchically-smoothed user preferences
Proceedings of the sixth ACM international conference on Web search and data mining
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
Focused matrix factorization for audience selection in display advertising
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Pairwise algorithms are popular for learning recommender systems from implicit feedback. For each user, or more generally context, they try to discriminate between a small set of selected items and the large set of remaining (irrelevant) items. Learning is typically based on stochastic gradient descent (SGD) with uniformly drawn pairs. In this work, we show that convergence of such SGD learning algorithms slows down considerably if the item popularity has a tailed distribution. We propose a non-uniform item sampler to overcome this problem. The proposed sampler is context-dependent and oversamples informative pairs to speed up convergence. An efficient implementation with constant amortized runtime costs is developed. Furthermore, it is shown how the proposed learning algorithm can be applied to a large class of recommender models. The properties of the new learning algorithm are studied empirically on two real-world recommender system problems. The experiments indicate that the proposed adaptive sampler improves the state-of-the art learning algorithm largely in convergence without negative effects on prediction quality or iteration runtime.