Individual Friends Recommendation Based on Random Walk with Restart in Social Networks

Abstract

In social networks, current friend/user recommendation methods are mainly based on similarity measurements among users or the structure of social networks. In this paper, we design a novel friend recommendation method according to a new individual feature intimacy degree. Intimacy degree reflects the degree of interaction between two users and further indicates how close two users pay attention to each other. Specifically, we first formally define this problem and perform a theoretical investigation of the problem based on random walk with restart model. And then we design an individual friend recommendation algorithm based on the social structures and behaviors of users. At last, we conduct experiments to verify the method on a real social data set. Experimental results show that the performance of friend recommendation outperforms the existing methods, and the proposed algorithm is effective and efficient in terms of PV Value, UV Value and Conversion Rate.