Preference-Aware Community Detection for Item Recommendation

In recent years, researches on recommendation systems based on social information have attracted a lot of attentions. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends´ rating behaviors. It leads to the problem that the recommended item list is usually constrained within the users´ or friends´ living area. Furthermore, since context-aware and environmental information changes quickly, especially in social networks, how to select appropriate relevant users from such kind of heterogeneous social structure to facilitate the social-based recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Preference-aware Community-based Recommendation System (PCRS) that integrates Preference-aware Community Detection (PCD) for recommending items to users based on the user preferences and social network structure simultaneously. The core idea of PCRS is to build a community-based collaborating filtering model in the user-to-item matrix, so as to support the estimation of users´ rating for each item. Based on the social network data, we detect communities through users´ Social Factor and Individual Preference for our community-based collaborating filtering model. To our best knowledge, this is the first work on community-based collaborating filtering model that considers both social factor and individual preference in social network data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Go Walla, the proposed PCRS is shown to deliver excellent performance.