Cost-sensitive three-way recommendations by learning pair-wise preferences

Abstract

Recommender systems aim to identify items that a user may like. In this paper, we discuss a three-way decision approach which provides a more meaningful way to recommend items to a user. Besides recommended items and not recommended items, the proposed model adds a set of items that are possibly recommended to users. In the model, we focus on two issues. One is the computation of required thresholds to define the three sets based on the decision-theoretic rough set model. The other is the notion of user preference on the three sets which forms the basis of a ranking strategy, and then a pair-wise preference learning algorithm using gradient descent is adopted for inferring latent vectors for users and items. Working with a sigmoid function of a product of a user and item latent vector, we estimate the probability that the user prefers the item to make recommendations. Experimental results show that the proposed method improves recommendation quality from the cost-sensitive view.