A unified microblog user similarity model for online friend recommendation

Abstract

Nowadays, people usually like to extend their real-life social relations into the online virtual social networks. With the blooming of Web 2.0 technology, huge number of users aggregate in the microblogging services, such as Twitter and Weibo, to express their opinions, record their personal lives and communicate with each other. How to recommend potential good friends for the target user has been a critical problem for both commercial companies and research communities. The key issue for online friend recommendation is to design an appropriate algorithm for user similarity measurement. In this paper, we propose a novel microblog user similarity model for online friend recommendation by linearly combining multiple similarity measurements of microblogs. Our proposed model can give a more comprehensive understanding of the user relationship in the microblogging space. Extensive experiments on a real-world dataset validate that our proposed model outperforms other baseline algorithms by a large margin.