| J. Callan. Information filtering with inference networks. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 262--269. Association for Computing Machinery, New York, Aug. 1996. |
....marked relevant by the user and the non relevant articles. This learned profile, also known as the routing or the feedback query since it is obtained using user s relevance feedback, is then matched against all the new articles that a system encounters (for example any new news stories, [5, 28, 2, 9] If a new article matches the user profile adequately, then this article is assumed to be of potential interest to the user and is routed to the user. The matching algorithm used by most systems to match a new article to a user profile is relatively straight forward. Most current IR systems use ....
J. Callan. Information filtering with inference networks. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 262--269. Association for Computing Machinery, New York, Aug. 1996.
....used for evaluating text filtering effectiveness, and do not use it in this study. Some other measures that have been used to evaluate text filtering are: ffl Average precision, or precision at a fixed rank cutoff: Many studies have used one of these measures to evaluate filtering effectiveness [2, 40, 41, 22, 1, 7]. These measures are intended to evaluate the ranking effectiveness of a system [31] not its filtering effectiveness. Even though the filtering effectiveness of a system is related to its ranking effectiveness, this relationship is not strong enough to use ranking evaluation measures to evaluate ....
J.P. Callan. Information filtering with inference networks. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages262--269.Association for Computing Machinery,New York, August 1996.
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