Community-Based Recommendations: a Solution to the Cold Start Problem

The “Cold-Start” problem is a well-known issue in recommendation systems: there is relatively little information about each user, which results in an inability to draw inferences to recommend items to users. In this paper, we try to give a solution to this problem based on homophily in social networks: we can use social networks’ information in order to fill the gap existing in cold-start problem and find similarities between users. In this study, we use communities, extracted from different dimensions of social networks, to capture the similarities of these different dimensions and accordingly, help recommendation systems to work based on the found latent similarities. By different dimensions, we mean friendship network, item similarity network, commenting network and etc.