Learning user's preferences by analyzing Web-browsing behaviors
Young-Woo Seo,Byoung-Tak Zhang +1 more
- pp 381-387
TLDR
The proposed method observes user's reactions to the filtered documents and learns from them the profiles for the individual users and reinforcement learning is used to adapt the most significant terms that best represent user's interests.Abstract:
This paper describes a method for an information filtering agent to learn user's preferences. The proposed method observes user's reactions to the filtered documents and learns from them the profiles for the individual users. Reinforcement learning is used to adapt the most significant terms that best represent user's interests. In contrast to conventional relevance feedback methods which require explicit user feedbacks, our approach learns user preferences implicitly from direct observations of browsing behaviors during interaction. Field tests have been made which involved 10 users reading a total of 18,750 HTML documents during 45 days. The proposed method showed superior performance in personalized information filtering compared to the existing relevance feedback methods.read more
Citations
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Machine learning
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Diane Kelly,Nicholas J. Belkin +1 more
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Using the taxonomy of cognitive learning to model online searching
TL;DR: The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels, and indicate that a learning theory may better describe the information searching process than more commonly used paradigms of decision making or problem solving.
References
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Journal ArticleDOI
Agents that reduce work and information overload
TL;DR: Results from several prototype agents that have been built using an approach to building interface agents are presented, including agents that provide personalized assistance with meeting scheduling, email handling, electronic news filtering, and selection of entertainment.