[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.
洪亮, 冉从敬. 主题相关的虚拟读者社区推荐方法研究[J]. 现代图书情报技术, 2014, 30(9): 51-57.
Hong Liang, Ran Congjing. Research on Topic Related Recommendation Method for Virtual Reader Community. New Technology of Library and Information Service, 2014, 30(9): 51-57.
[1] 胡昌平, 乐庆玲. 高校图书馆虚拟社区构建初探[J]. 现代图书情报技术, 2007(11): 87-90. (Hu Changping, Yue Qingling. Probe into the Construction of Virtual Community in Academic Library [J]. New Technology of Library and Information Service, 2007(11): 87-90.)
[2] 刘炜, 葛秋妍. 从 Web 2.0到图书馆2.0: 服务因用户而变[J]. 现代图书情报技术, 2006(9): 8-12. (Liu Wei, Ge Qiuyan. Library 2.0: Mashup Your Service on the Demand of Users [J]. New Technology of Library and Information Service, 2006(9): 8-12.)
[3] Adomavicius G, Tuzhilin A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions [J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[4] Pazzani M J. A Framework for Collaborative, Content-based and Demographic Filtering [J]. Artificial Intelligence Review, 1999, 13(5-6): 393-408.
[5] Goldberg D, Nichols D, Oki B M, et al. Using Collaborative Filtering to Weave an Information Tapestry [J]. Communi-cations of the ACM, 1992, 35(12): 61-70.
[6] Baatarjav E A, Phithakkitnukoon S, Dantu R. Group Recommendation System for Facebook [A]//On the Move to Meaningful Internet Systems: OTM 2008 Workshops [C]. Springer Berlin Heidelberg, 2008: 211-219.
[7] Kim H K, Oh H Y, Gu J C, et al. Commenders: A Recommendation Procedure for Online Book Communities [J]. Electronic Commerce Research and Applications, 2011, 10(5): 501-509.
[8] Zheng N, Li Q, Liao S, et al. Flickr Group Recommendation Based on Tensor Decomposition [C]. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2010: 737-738.
[9] Chen W Y, Chu J C, Luan J, et al. Collaborative Filtering for Orkut Communities: Discovery of User Latent Behavior [C]. In: Proceedings of the 18th International Conference on World Wide Web. ACM, 2009: 681-690.
[10] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[11] 陈琼, 李辉辉, 肖南峰. 基于节点动态属性相似性的社会网络社区推荐算法[J]. 计算机应用, 2010, 30(5): 1268-1272. (Chen Qiong, Li Huihui, Xiao Nanfeng. Community Recommendation Algorithm Based on Dynamic Attributes Similarity of Nodes in Social Networks [J]. Journal of Computer Applications, 2010, 30(5): 1268-1272.)
[12] Yu Q, Peng Z, Hong L, et al. Novel Community Recom-mendation Based on a User-Community Total Relation [C]. In: Proceedings of the 19th International Conference on Database Systems for Advanced Applications. Springer International Publishing, 2014: 281-295.
[13] Hofmann T, Puzicha J. Latent Class Models for Collaborative Filtering [C]. In: Proceeding of the 16th International Joint Conference on Artificial Intelligence. 1999: 688-693.