Abstract
We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user’s preferences and interests. In this paper we focus on the system’s user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of explicitly modeling information that the system has already presented to the user. This allows us to address an important issue that has thus far received virtually no attention in the Information Retrieval community: the fact that a user’s information need changes as a direct result of interaction with information. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the individual performance contributions of both model components.
We thank Daimler-Benz and Sun Microsystems, Inc. for their generous support.
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© 1999 Springer Science+Business Media New York
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Billsus, D., Pazzani, M.J. (1999). A Hybrid User Model for News Story Classification. In: Kay, J. (eds) UM99 User Modeling. CISM International Centre for Mechanical Sciences, vol 407. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2490-1_10
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DOI: https://doi.org/10.1007/978-3-7091-2490-1_10
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83151-9
Online ISBN: 978-3-7091-2490-1
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