A multi-agent based model for user interest mining on Sina Weibo

User interest mining on Sina Weibo is the basis of personalized recommendations, advertising, marketing promotions, and other tasks. Although great progress has been made in this area, previous studies have ignored the differences among users: the varied behaviors and habits that lead to unique user data characteristics. It is unreasonable to use a single strategy to mine interests from such varied user data. Therefore, this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data, microblogs and correlations. This method has the ability to select the appropriate strategy based on each user's data characteristics. The experimental results show that the proposed method performs better than the baselines.