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2004
Springer

Learning Users' Interests in a Market-Based Recommender System

9 years 11 months ago
Learning Users' Interests in a Market-Based Recommender System
Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. Our marketplace thus coordinates multiple recommender agents and ensures only the best recommendations are presented. To do this effectively, however, each agent needs to learn the users’ interests and adapt its recommending behaviour accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommender agents can use for these tasks. We then demonstrate that this strategy helps the agents to effectively obtain information about the users’ interests which, in...
Yan Zheng Wei, Luc Moreau, Nicholas R. Jennings
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where IDEAL
Authors Yan Zheng Wei, Luc Moreau, Nicholas R. Jennings
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