A social recommendation framework based on multi-scale continuous conditional random fields

Abstract

This paper addresses the issue of social recommendation based on collaborative filtering (CF) algorithms. Social rec-ommendation emphasizes utilizing various attributes infor-mation and relations in social networks to assist recom-mender systems. Although recommendation techniques have obtained distinct developments over the decades, traditional CF algorithms still have these following two limitations: (1) relational dependency within predictions, an important fac-tor especially when the data is sparse, is not being uti-lized effectively; and (2) straightforward methods for com-bining features like linear integration suffer from high com-puting complexity in learning the weights by enumerating the whole value space, making it difficult to combine var-ious information into an unified approach. In this paper, we propose a novel model, Multi-scale Continuous Condi-tional Random Fields (MCCRF), as a framework to solve above problems for social recommendations. In MCCRF, relational dependency within predictions is modeled by the Markov property, thus predictions are generated simultane-ously and can help each other. This strategy has never been employed previously. Besides, diverse information and rela-tions in social network can be modeled by state and edge feature functions in MCCRF, whose weights can be opti-mized globally. Thus both problems can be solved under this framework. In addition, We propose to utilize Markov chain Monte Carlo (MCMC) estimation methods to solve the difficulties in training and inference processes of MCCRF. Experimental results conducted on two real world data have demonstrated that our approach outperforms traditional CF algorithms. Additional experiments also show the improve-ments from the two factors of relational dependency and feature combination, respectively