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Competitive Agents For Information Filtering
Published 1992 · Computer Science
(See Table 2 for rule examples.) Many of the rules were shared by more than one user. Such rules could form the basis Of stereotypes, which could be used as initial filters for new users. Preliminary results also show that users employed a small number Of goal types and message types in their rules, and that each user's rules were consistent among themselves. There were never two rules where the same conjunctive clause implied that a user would both read and not read a message. Also, if more than one rule applied to a ruessage, all Of the rules would imply the same reading decision. This provides a basis for automating rule learning because many machine-learning algorithms require little or no noise in the data set (e.g., no contradictions). Some of the goals and domain concepts news readers used to describe messages were more abstract and will be more difficult to acquire for the user models. Abstract goals (e.g., look in documentation, ignore impossible configurations) and abstract domain categories (e.g., actually experienced, factual, popular, easy, new, informative) are more difficult to find or parse from a message and are harder to acquire from the user because they develop as the user reads messages and cannot be known beforehand. Thus, the abstract categories cannot be used in the initial phase of knowledge acquisition when the user first specifies his or her user model. In the study, news readers used them to describe the message and their reading decision for as many as one-third of the messages. These categories will require more elaborate representation and acquisition techniques than those currently used for the user models. It is important for information filters to model users' interests, though it is difficult to keep such models accurate. However, there appear to be some things that can be done to make such models and information filters based on them more useful. [ ] 1. Furnas, G.W., Landauer, T.K., Gomez, L.M. and Dumais, S.T. The vocabulary problem in human-system communication. Commun. A C M , 30(11):964-971, 1987. 2. Gaines, B.R. and Shaw, M.L.G. Comparing the conceptual systems of experts. In Eleventh International Conference on Artif icial Intelligence (1989), pp. 633-638. 3. Stadnyk, I. and Kass, R. Modeling users' interests in information filters. Tech. Rep. CFAR-92-006, Center for Advanced Research, 1992.