User-level microblogging recommendation incorporating social influence

详细信息    查看全文

• 作者：Daifeng Li ; Zhipeng Luo ; Ying Ding ; Jie Tang ; Gordon Guo-Zheng Sun ; Xiaowen Dai ; John Du ; Jingwei Zhang and Shoubin Kong
• 刊名：Journal of the American Society for Information Science and Technology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：68
• 期：3
• 页码：553-568
• 全文大小：851K
• ISSN：1532-2890

文摘

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.