Home | Browse | Search | Credits | About
Register | User Area | DL-Harvest | Help
DLIST

A Graph-based Recommender System for Digital Library

Huang, Zan and Chung, Wingyan and Ong, Thian-Huat and Chen, Hsinchun (2002) A Graph-based Recommender System for Digital Library. In Proceedings Joint Conference on Digital Libraries, pages pp. 65-73, Portland, OR.

Full text available as:
PDF - Requires Adobe Acrobat Reader or other PDF viewer.

Abstract

Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, useruser and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.

EPrint Type:Conference Paper
Keywords:National Science Digital Library, NSDL, Artificial Intelligence Lab, AI Lab, Recommender system, Hopfield net algorithm, Graph-based model, Content-based filtering, Collaborative filtering, Mutual Information algorithm, Chinese phrase extraction
Subjects:Evaluation
Digital Libraries
ID Code:428
Deposited On:20 August 2004
Alternative Locations:http://ai.bpa.arizona.edu/go/papers.html
Eprint Statistics:View statistics for this eprint
Tell A Colleague:Tell a colleague about it.

[1] Ahmad, M., Wasfi, A., Collecting User Access Patterns for Building User Profiles and collaborative Filtering. in Proceedings of the 1999 International Conference on Intelligent User Interfaces, (1999), 57-64.

[2] Baeza-Yates, R., Gonnet, G. Fast Text Searching for Regular Expressions or Automaton Searching on Tries. Journal of the ACM, 43 (6), (1996), 915-936.

[3] Balabanovic, M., Shoham, Y. Content-based, collaborative recommendation. Communications of the ACM, 40 (3), (1997), 66-72.

[4] Basu, C., Hirsh, H. Cohen, W., Nevill-Manning, C. Technical Paper Recommendation: A Study in Combining Multiple Information Sources. Journal of Artificial Intelligence Research, (2001). 231-252.

[5] Basu, C., Hirsh, H., Cohen, W., Recommendation as classification: Using social and content-based information in recommendation. in Proceeding of the AAAI-98, (Madison, WI, 1998), AAAI Press, 714-720.

[6] Chen, H., Ng, T. An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound Search vs. Connectionist Hopfield Net Activation. Journal of the American Society for Information Science, 46 (5), (1995). 348-369.

[7] Church, K., A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text. in Proceedings of the Second Annual Conference on Applied Natural Language Parsing ACL, (Austin, TX, 1988).

[8] Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M., Combining Content-Based and Collaborative Filters in an Online Newspaper. in Proceedings of ACM SIGIR Workshop on Recommender Systems, (1999).

[9] Condliff, M.K., Lewis, D., Madigan, D., Posse, Bayesian, C., Mixed-effects Models for Recommender Systems. in Proceedings of ACM SIGIR Workshop on Recommender Systems, (1999).

[10] Dalton, J., Deshmane, A. Artificial neural networks. IEEE Potentials, 10 (2), (1991). 33-36.

[11] Geisler, G., McArthur, D., Giersch, S., Developing recommendation services for a digital library with uncertain and changing data. in Proceedings of the first ACM/IEEE-CS Joint Conference on Digital libraries, (Roanoke, VA, United States, 2001), 199 - 200.

[12] Hill, W., Stead, L., Rosenstein, M., Furnas, G., Recommending and evaluating choices in a virtual community of use. in Proceedings of the Computer- Human Interaction Conference, (Denver, CO, 1995), ACM Press, 194-201.

[13] Houston, A.L., Chen, H., Schatz, B.R., Hubbard, S.M., Sewell, R., Ng, T. Exploring the use of concept spaces to improve medical information retrieval. Decision Support Systems, 30 (2), (2000). 171-186.

[14] Knight, K. Connectionist ideas and algorithms. Communications of the ACM, 33 (11), (1990). 59-74.

[15] Kwok, K., Comparing Representations in Chinese Information Retrieval. in Proceedings of ACM SIGIR, (1997), 34-41.

[16] Manber, U., Myers, G. Suffix arrays: a new method for on-line string searches. SIAM-Journal-on-Computing, 22 (5), (1993). 935-948.

[17] Mooney, R., Roy, L., Content-based book recommending using learning for text categorization. in Proceedings of the Fifth ACM Conference on Digital Libraries, (2000), 195-204.

[18] Ong, T., Chen, H., Updateable PAT-Tree approach to Chinese key phrase extraction using mutual information: a linguistic foundation for knowledge management. in Proceedings of the Second Asian Digital Library Conference, (Taipei, Taiwan, 1999), 63-84.

[19] Pazzani, M. A Framework for Collaborative, ContentBased and Demographic Filtering. Artificial Intelligence Review, (1999), 393-408.

[20] Resnick, P., Varian, H. Recommender Systems. Communications of the ACM, 40 (3), (1997). 56-58.

[21] Salton, G. Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer. Addison Wesley, Reading, MA, 1989.

[22] Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., Riedl, J., Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System. in Proceedings of the the ACM Conference on computer Supported Cooperative Work (CSCW), (1998).

[23] Schafer, J., Konstan, J., Riedl, J. E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 5 (1-2), (2001). 115-153.

[24] Shardanand, U., Maes, P., Social Information Filtering: Algorithms for Automating 'Word of Mouth'. in Proceedings of the Computer-Human Interaction Conference, (Denver, CO, 1995), ACM Press, 210-217.

EPrints dLIST, an open access archive for the Information Sciences, is supported by the School of Information Resources and Library Science and Learning Technologies Center, University of Arizona. Established in 2002, dLIST has a global Advisory Board and is a part of the Information Technology & Society Research Lab. Open Archives
Contact: Admin | Donate