Library Book Recommendations Based on Latent Topic Aggregation

详细信息    查看全文

• 关键词：book suggestion ; latent topic ; library service ; collaborative filtering
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8839
• 期：1
• 页码：411-416
• 全文大小：154 KB
• 参考文献：1. Kent, A., et al.: Use of library materials: the university of Pittsburgh study. Pittsburgh University, PA (1979)
  2. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science?41(6), 391-07 (1990) CrossRef
  3. Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning?42(1), 177-96 (2001) CrossRef
  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. Journal of Machine Learning Research?3(5), 993-022 (2003)
  5. Girolami, M., Kaban, A.: On an equivalence between PLSI and LDA. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 433-34 (2003)
  6. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys?31, 264-23 (1999) CrossRef
  7. Widyantoro, D., Ioerger, T.R., Yen, J.: An Incremental Approach to Building a Cluster Hierarchy. In: Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM 2002 (2002)
  
• 作者单位：Shun-Hong Sie (18)
  Jian-Hua Yeh (19)
  
  18. Graduate Institute of Library & Information Studies, NTNU, Norway
  19. Department of Computer Science and Information Engineering, Aletheia University, Taiwan
  
• ISSN：1611-3349

文摘

During recent years, how to provide personalized services has become an important research issue in library services. The libraries provide more and more personalized services such as customized web interface and reading suggestions. In the traditional approaches, the features of the books that a reader likes are used to construct the profile of the reader to support recommendation of books such as query keywords. But with the fact of the huge holdings in the libraries, the librarians need to effectively help the readers to find the books of interest. Collaborative filtering (CF) is a way to make it possible by use patron’s circulation logs which contain their borrow history as favorite readings. In this paper, we first use Latent Dirichlet Allocation to find the latent topics existing in the circulation logs, then we combine patron reading histories with the generated latent topics to produce a suggestion list for the patron. With the elaborated experiments demonstrated in this paper, it showed good results from the volunteers-feedback.