How to Learn More about Users from Implicit Observations1
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In this paper, an approach to learning user interests is presented. It relies on positive evidences only, in consideration of the fact that users rarely supply the ratings needed by traditional learning algorithms, specifically not negative examples. Learning results are explicitly represented to account for the fact that in the area of user modeling explicit representations are known to be considerably more useful than purely implicit representations. A content-based recommendation approach is presented. The described framework has been extensively tested in an information system.
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- 1.Lieberman H. (1995). Letizia: An Agent That Assists Web Browsing. International Joint Conference on Artificial Intelligence, Montreal.Google Scholar
- 2.Mladenic D. (1996). Personal WebWatcher: Implementation and Design. Technical Report IJS-DP-7472, Department of Intelligent Systems, J. Stefan Institute, Slovenia.Google Scholar
- 4.Schwab I., Pohl W. and Koychev, I. (2000). Learning to Recommend from Positive Evidence, Proceedings of Intelligent User Interfaces 2000, ACM Press, pp. 241–247.Google Scholar
- 5.Schwefel H.-P. Evolution And Optimum Seeking. John Wiley & Sons, Inc. 1995.Google Scholar
- 6.Michael S., Beilken Ch.: Visual, Interactive Data Minung with InfoZoom-the Financial Data Set. In: Workshop Notes on Discovery Challenge. Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD’ 99, September 15-18, 1999, Prague, Czech Republic, page 33–38. http://fit.gmd.de/~cici/InfoZoom/DiscoveryChallenge/Financial.ps