An Adaptive Method for the Tag-Rating-Based Recommender System

摘要

In this paper, we propose an adaptive method for recommender system based on users' preference to items represented by the ratings of users. This method defines a term-association matrix to describe the relation between tags and items properties. A gradient descent method is employed to compute the association matrix. The association matrix is then used to implement the two kinds of recommendation, namely, tag recommendation and items properties recommendation.