Zan Huang
Wingyan Chung
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, user-user 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.
- 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 Google Scholar
Digital Library
- 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 Google ScholarDigital Library
- Balabanovic, M., Shoham, Y. Content-based, collaborative recommendation. Communications of the ACM, 40 (3), (1997), 66--72 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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) Google ScholarDigital Library
- 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)Google Scholar
- 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)Google Scholar
- Dalton, J., Deshmane, A. Artificial neural networks. IEEE Potentials, 10 (2), (1991). 33--36Google ScholarCross Ref
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- Knight, K. Connectionist ideas and algorithms. Communications of the ACM, 33 (11), (1990). 59--74 Google ScholarDigital Library
- Kwok, K., Comparing Representations in Chinese Information Retrieval. in Proceedings of ACM SIGIR, (1997), 34--41 Google ScholarDigital Library
- Manber, U., Myers, G. Suffix arrays: a new method for on-line string searches. SIAM-Journal-on-Computing, 22 (5), (1993). 935--948 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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--84Google Scholar
- Pazzani, M. A Framework for Collaborative, ContentBased and Demographic Filtering. Artificial Intelligence Review, (1999), 393--408 Google ScholarDigital Library
- Resnick, P., Varian, H. Recommender Systems. Communications of the ACM, 40 (3), (1997). 56--58 Google ScholarDigital Library
- Salton, G. Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer. Addison Wesley, Reading, MA, 1989 Google ScholarDigital Library
- 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) Google ScholarDigital Library
- Schafer, J., Konstan, J., Riedl, J. E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 5 (1-2), (2001). 115--153 Google ScholarDigital Library
- 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 Google ScholarDigital Library
Index Terms
- A graph-based recommender system for digital library
Recommendations
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Exploiting Social Tagging in a Web 2.0 Recommender System
Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...
Comments