主题相关的虚拟读者社区推荐方法研究

Research on Topic Related Recommendation Method for Virtual Reader Community

【目的】为了帮助读者从海量的虚拟读者社区中选择符合其兴趣的社区。【方法】提出基于主题概率模型的读者社区推荐方法,通过发现读者社区的隐含主题,建立起读者与读者社区在不同主题上的联系,并根据社区和读者的主题相似度进行读者社区推荐。【结果】在真实数据上的实验证明该方法能够有效地发现读者社区的隐含主题,相比现有的推荐方法,能够准确地推荐虚拟读者社区。【局限】存在推荐的冷启动问题。【结论】该推荐方法帮助读者准确迅速地找到感兴趣的主题相关虚拟读者社区,能够促进读者的沟通交流和虚拟读者社区的发展。

[Objective] To help readers select interested communities from massive reader communities. [Methods] This paper proposes virtual reader community recommendation method based on probabilistic topic model, which builds reader-reader and reader-community relations on different topics by finding latent topics of reader communities, and then recommends reader communities by considering topic similarities of both communities and readers. [Results] Experiments on real data prove that the method can effectively find latent topics of reader communities and accurately recommend virtual reader communities compared with existing recommendation methods. [Limitations] Exist cold start problem of recommendation. [Conclusions] This method helps readers accurately and quickly find interested topic-related virtual reader community, promoting the communication of readers and the development of virtual reader communities.