Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th 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
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Fast als-based matrix factorization for explicit and implicit feedback datasets
Proceedings of the fourth ACM conference on Recommender systems
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of recommendations: rating-prediction and ranking
Proceedings of the 7th ACM conference on Recommender systems
A survey of collaborative filtering based social recommender systems
Computer Communications
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Matrix factorization (MF) has evolved as one of the most accurate approaches to collaborative filtering. In this paper, we extend the probabilistic MF framework as to account for multiple observations for each matrix element. This significantly improves the accuracy of recommender systems in several areas: (1) aggregation of ratings concerning items organized hierarchically, (2) (partial) compensation for the selection bias in the observed data by using an appropriate prior with virtual data points, and (3) improved recommendations of TV shows. While our framework applies to explicit and implicit feedback data, we outline in detail the latter application in this paper: we present the first approach that takes into account also negative feedback when training on implicit feedback data. Moreover, we shed light on the implicit assumptions underlying the most successful approach to IP-TV (Internet Protocol Television) recommendations in [Hu et al. 2008]. In our experiments, we obtain significant improvements over the existing approach.