Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Restricted Boltzmann machines for collaborative filtering
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Proceedings of the 7th ACM conference on Recommender systems
CLiMF: collaborative less-is-more filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Recommendations from the long tail of the popularity distribution of items are generally considered to be particularly valuable. On the other hand, recommendation accuracy tends to decrease towards the long tail. In this paper, we quantitatively examine this trade-off between item popularity and recommendation accuracy. To this end, we assume that there is a selection bias towards popular items in the available data. This allows us to define a new accuracy measure that can be gradually tuned towards the long tail. We show that, under this assumption, this measure has the desirable property of providing nearly unbiased estimates concerning recommendation accuracy. In turn, this also motivates a refinement for training collaborative-filtering approaches. In various experiments with real-world data, including a user study, empirical evidence suggests that only a small, if any, bias of the recommendations towards less popular items is appreciated by users.