Top-k followee recommendation over microblogging systems by exploiting diverse information sources

Followee recommendation plays an important role in information sharing over microblogging platforms. We frame this problem as a top-k ranking in collaborative filtering (CF). The difficulty is that explicit user-to-user ratings are not available on microblogging systems. Thus existing CF schemes are not applicable to followee recommendation over microblogging systems. To solve this problem, in this paper, we propose a novel followee ranking scheme using a variation of the latent factor model, which leverages implicit users’ feedback including both tweet content and social relation information. To achieve good top-k recommendation, we introduce a rank-based criterion to latent factor model (LFM). The main obstacle for training the model parameters is the non-smoothness of the objective function of LFM, which makes traditional parameter optimization methods infeasible. To tackle with the problem, we further design a smooth version of the objective function. We conduct comprehensive experiments on a large-scale dataset collected from Sina Weibo, the most popular microblogging system in China and a real world experiment on the Amazon Mechanical Turk CrowdSourcing platform to evaluate the performance of our design. The results show that our scheme greatly outperforms existing schemes in terms of precision and top-k ranking by 46.8% and 32.8%, respectively.