Estimating confidence of individual rating predictions in collaborative filtering recommender systems

Collaborative filtering algorithms predict a rating for an item based on the user’s previous ratings for other items as well as ratings of other users. This approach has gained notable popularity both in academic research and in commercial applications. One aspect of collaborative filtering systems that received interest, but little systematic analysis, is confidence of the rating predictions by collaborative filtering algorithms. In this paper, I address this issue. Specifically: (1) I offer a discussion on the definition of confidence, (2) I propose a method for evaluating performance of confidence estimation algorithms, and (3) I evaluate six different confidence estimation algorithms. Three of those algorithms are introduced in this paper and three have been previously suggested for this purpose. The comparative experimental evaluation demonstrates that two of the algorithms proposed in this study: one using resampling of available ratings and one using noise injection to the available ratings provide the best performance in terms of separation between predictions of high and low confidence. The algorithms that use only the number of ratings available for the user of interest or for the item of interest turned out to be of limited use for confidence estimation.