A random-walk based recommendation algorithm considering item categories

Abstract

Recommender systems aim at recommending information items or social elements that are likely to be of interest to users. In this paper, we propose a recommendation algorithm which takes into account user's preference on item categories, and computes rank scores in different categories for each item, in order to make suggestions based on both user's previous interactions and item contents. By considering item categories and user preference, we are able to avoid the dominance of some popular items. Empirical experiments on MovieLens dataset demonstrate that the algorithm outperforms other state-of-the-art recommendation algorithms.