Statistical analysis with missing data
Statistical analysis with missing data
IEEE Transactions on Knowledge and Data Engineering
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
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
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
Matrix Completion from Noisy Entries
The Journal of Machine Learning Research
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Item popularity and recommendation accuracy
Proceedings of the fifth ACM conference on Recommender systems
Multi-value probabilistic matrix factorization for IP-TV recommendations
Proceedings of the fifth ACM conference on Recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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The literature on recommender systems distinguishes typically between two broad categories of measuring recommendation accuracy: rating prediction, often quantified in terms of the root mean square error (RMSE), and ranking, measured in terms of metrics like precision and recall, among others. In this paper, we examine both approaches in detail, and find that the dominating difference lies instead in the training and test data considered: rating prediction is concerned with only the observed ratings, while ranking typically accounts for all items in the collection, whether the user has rated them or not. Furthermore, we show that predicting observed ratings, while popular in the literature, only solves a (small) part of the rating prediction task for any item in the collection, which is a common real-world problem. The reasons are selection bias in the data, combined with data sparsity. We show that the latter rating-prediction task involves the prediction task 'Who rated What' as a sub-problem, which can be cast as a classification or ranking problem. This suggests that solving the ranking problem is not only valuable by itself, but also for predicting the rating value of any item.