Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Improving Recommender Systems by Incorporating Social Contextual Information
ACM Transactions on Information Systems (TOIS)
Response prediction using collaborative filtering with hierarchies and side-information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Impact of data characteristics on recommender systems performance
ACM Transactions on Management Information Systems (TMIS)
Expert Systems with Applications: An International Journal
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Many real life datasets have skewed distributions of events when the probability of observing few events far exceeds the others. In this paper, we observed that in skewed datasets the state of the art collaborative filtering methods perform worse than a simple probabilistic model. Our test bench includes a real ad click stream dataset which is naturally skewed. The same conclusion is obtained even from the popular movie rating dataset when we pose a binary prediction problem of whether a user will give maximum rating to a movie or not.