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| Research on Topic Related Recommendation Method for Virtual Reader Community |
| Hong Liang, Ran Congjing |
| School of Information Management, Wuhan University, Wuhan 430072, China |
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Abstract [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.
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Received: 26 May 2014
Published: 20 October 2014
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