Membership in social networks and the application in information filtering

TL;DR

This paper investigates user membership in online social networks and their influence on information filtering, proposing a social diffusion recommendation algorithm that improves cold-start user recommendations by leveraging membership and object selection data.

Contribution

It introduces a novel analysis of user membership influence on object selection and develops a social diffusion algorithm to enhance recommendation accuracy, especially for cold-start users.

Findings

Users with few objects are more influenced by memberships.

Some users join many communities despite few objects.

The proposed method improves recommendation performance for cold-start users.

Abstract

During the past a few years, users' membership in the online system (i.e. the social groups that online users joined) are wildly investigated. Most of these works focus on the detection, formulation and growth of online communities. In this paper, we study users' membership in a coupled system which contains user-group and user-object bipartite networks. By linking users' membership information and their object selection, we find that the users who have collected only a few objects are more likely to be "influenced" by the membership when choosing objects. Moreover, we observe that some users may join many online communities though they collected few objects. Based on these findings, we design a social diffusion recommendation algorithm which can effectively solve the user cold-start problem. Finally, we propose a personalized combination of our method and the hybrid method in [PNAS 107, 4511 (2010)], which leads to a further improvement in the overall recommendation performance.