IEEE Transactions on Knowledge and Data Engineering
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
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Fast als-based matrix factorization for explicit and implicit feedback datasets
Proceedings of the fourth ACM conference on Recommender systems
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection
Proceedings of the 7th ACM conference on Recommender systems
Personalised ranking with diversity
Proceedings of the 7th ACM conference on Recommender systems
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Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user-item preference relationship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the system has to infer user preferences from indirect information: presence or absence of events, such as a user viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional prediction mean squared error. The naive minimization of a ranking objective function is typically expensive. This difficulty is usually overcome by a trade-off: sacrificing the accuracy to some extent for computational efficiency by sampling the objective function. In this paper, we present a computationally effective approach for the direct minimization of a ranking objective function, without sampling. We demonstrate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.