Marko Balabanović
Yoav Shoham
- 1 Balabanovic, M. An adaptive web page recommendation service. In Proceedings of the 1st International Conj%ence on A~tonomom Agents (Marina del Rey, Calif., Feb. 1997). Google Scholar
Digital Library
- 2 Buckley, C., and Salton, G. Optimization of relevance feedback weights. In Proceedings of the 18th Annual International ACM SIGIR Conj%ence on Research and Development in InJbrmation Retrieval (Seattle, July 1995). Google ScholarDigital Library
- 3 Harman, D. Overview of the 3rd Text REtrieval Conference (TREC-3). In Proceedings of the 3rd Text REtrieval Conj%ence (Gaithersburg, Md, Nov. 1994).Google Scholar
- 4 Hill, W., Stead, L., Rosenstein, M., and Furnas, G. Recommending and evaluating choices in a virtual community of use. In Conjbrence on Human Factors in Computing Systems--~HI '95. (Denver, May 1995). Google ScholarDigital Library
- 5 Krulwich, B., and Burkey, C. Learning user information interests through extraction of semantically significant phrases. In Proceedings of the AAAI Spring Symposium on Machine Learning in InJbrmation Access (Stanford, Calif., March 1996).Google Scholar
- 6 Lang, K. Newsweeder: Learning to filter netnews. In Proceedings of the 12th International Conjbrence on Machine Learning (Tahoe City, Calif.) 1995.Google Scholar
- 7 Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conj%ence on Computer-Supported Cooperative Work (Chapel Hill, NC) 1994. Google ScholarDigital Library
- 8 Shardanand, U., and Maes, P. Social information filtering: Algorithms for automating "word of mouth." In Conj~rence on Human Factors in Computing Systems--CHI '95. (Denver, May 1995). Google ScholarDigital Library
- 9 Sheth, B., and Maes, P. Evolving agents for personalized information filtering. In Proceedings of the 9th IEEE Conj%ence on Artificial Intelligence for Applications (Orlando, Fla, March 1993).Google ScholarCross Ref
- 10 Yao, Y. Y. Measuring retrieval effectiveness based on user preference of documents. J. Amer. Soc. Info. Sci. 46, 2 (1995), 133-145. Google ScholarDigital Library
Index Terms
- Fab: content-based, collaborative recommendation
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