Abstract

Information presented to a user via an information access system is ranked according to a prediction of the likely degree of relevance to the user's interests. A profile of interests is stored for each user having access to the system. Items of information to be presented to a user are ranked according to their likely degree of relevance to that user and displayed in order of ranking. The prediction of relevance is carried out by combining data pertaining to the content of each item of information with other data regarding correlations of interests between users. A value indicative of the content of a document can be added to another value which defines user correlation, to produce a ranking score for a document. Alternatively, multiple regression analysis or evolutionary programming can be carried out with respect to various factors pertaining to document content and user correlation, to generate a prediction of relevance. The user correlation data is obtained from feedback information provided by users when they retrieve items of information. Preferably, the user provides an indication of interest in each document which he or she retrieves from the system.

Other References

• Graphical Knowledge based electronic mail system by Kantardzic, M. et al., IEEE conference paper. pp. 1165-1168, May 24, 1919
• Evolving agents for personalized information filtering by Sheth B. et al. IEEE conference paper. pp. 345-352, Mar. 5, 1993
• A wide area network information filter by Wyle M. F. IEEE conference paper. pp. 10-15, Oct. 11, 1991
• Replication in an information filtering system by Terry D. B. IEEE conference paper. pp. 66-67, Nov. 13, 1992
• Visual tools for information retrieval by Spoerri A. IEEE conference paper. pp. 160-168, Aug. 27, 1993
• Using collaborative filtering to weave an information tapestry by Goldberg David et al. Communications of the ACM, pp. 61-70, Dec. 1991
• Scsior: Extracting information from online news by Jacobs P.S. et al. Communications of the association for computing machinery, pp. 88-97, Mar. 5, 1993
• M. Rothman, "A New Music Retailing Technology Says, `Listen Here`", The New York Times, Sunday, Jul. 4, 1993, pp. F8-9
• J. Karlgren, "Using Reader Data as a Basis for Measuring Document Proximity", An Algebra for Recommendations, pp. 1-9
• R.K. Belew, "Adaptive Information Retrieval: Using a connectionist representation to retrieve and learn about documents", 12th Int'l. Conference on Research & Development in IR, Jun. 1989
• T.W. Malone et al, "The Information Lens: An Intelligent System for Information sharing in Organizations", CHI '86 Proceedings, Apr. 1986, pp. 1-8
• G. Fischer et al, "Information Access in Complex, Poorly Structured Information Spaces", CHI '91 Proceedings, Apr.-May, 1991
• B. Sheth et al, "Evolving Agents for Personalized Information Filtering", Proceedings of the Ninth IEEE Conference on Artificial Intelligence for Applications, CAIA '93, Orlando, Florida, Mar. '93
• "Announcement of Bellcore Video Rating System"
• Goldberg, David et al, "Using Collaborative Filtering to Weave an Information Tapestry", Communications of the ACM, Dec. 1991, vol. 35, No. 12., pp. 61-70
• Jennings, Andrew et al, "A Personal News Service Based on a User Model Neural Network", IEICE Transactions on Information and Systems, Mar. 1992, No. 2, pp. 198-209
• Stanfill, Craig, "Massively Parallel Information Retrieval for Wide Area Information Servers", IEEE, Aug. 1991, pp. 679-68