Evolving agents for personalized information filtering Export

@proceedings{citeulike:1217954, abstract = {Describes how techniques from artificial life can be used to evolve a population of personalized information filtering agents. The technique of artificial evolution and the technique of learning from feedback are combined to develop a semi-automated information filtering system which dynamically adapts to the changing interests of the user. Results of a set of experiments are presented in which a small population of information filtering agents was evolved to make a personalized selection of news articles from the USENET newsgroups. The results show that the artificial evolution component of the system is responsible for improving the recall rate of the selected set of articles, while learning from feedback component improves the precision rate}, author = {Sheth, B. and Maes, P.}, citeulike-article-id = {1217954}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=366590}, journal = {Artificial Intelligence for Applications, 1993. Proceedings., Ninth Conference on}, keywords = {b, explicit\_feedback, personalization, recommendation}, pages = {345--352}, posted-at = {2007-04-09 15:40:58}, priority = {0}, title = {Evolving agents for personalized information filtering}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=366590}, year = {1993} }

TY - CONF ID - citeulike:1217954 L3 - citeulike-article-id:1217954 N2 - Describes how techniques from artificial life can be used to evolve a population of personalized information filtering agents. The technique of artificial evolution and the technique of learning from feedback are combined to develop a semi-automated information filtering system which dynamically adapts to the changing interests of the user. Results of a set of experiments are presented in which a small population of information filtering agents was evolved to make a personalized selection of news articles from the USENET newsgroups. The results show that the artificial evolution component of the system is responsible for improving the recall rate of the selected set of articles, while learning from feedback component improves the precision rate JF - Artificial Intelligence for Applications, 1993. Proceedings., Ninth Conference on EP - 352 TI - Evolving agents for personalized information filtering SP - 345 KW - b KW - explicit_feedback KW - personalization KW - recommendation AU - Sheth, B AU - Maes, P PY - 1993/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=366590 ER -