A generic Bayesian-based framework for enhancing top-N recommender algorithms

Abstract

Personalized top-N recommender algorithms have been investigated widely in decades. The core task of different recommender algorithms is to estimate user-item preference scores and then to suggest, for each user, top-N items that have high preference scores. However, little attention was paid to the recommendation of items with low preference scores. In this work, we use the bayesian estimation theory to build the relationship between the estimated preference scores and the actual recommendation accuracy. We then propose a novel metric RNR (Recall-to-Noise Ratio) to recommend items with low estimated preference scores. An interesting counterintuitive phenomenon is found that user-item links with low preference scores may achieve higher accuracy than high preference score ones in the recommendation. Based on RNR, we design a generic framework that could recommend user-item links that have low preference scores without the loss of recommendation accuracy. The effectiveness of the proposed framework is illustrated by both theoretical analysis and empirical experiments.