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User evaluation of a market-based recommender system

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Abstract

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommender.

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Authors and Affiliations

  1. Department of Broadband Wireless Management, Huawei, B1-F2-B, Huadian, Bantian, Shenzhen, 518219, China

    Yan Zheng Wei

  2. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Nicholas R. Jennings, Luc Moreau & Wendy Hall

Authors
  1. Yan Zheng Wei
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  2. Nicholas R. Jennings
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  3. Luc Moreau
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  4. Wendy Hall
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Corresponding author

Correspondence to Nicholas R. Jennings.

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Wei, Y.Z., Jennings, N.R., Moreau, L. et al. User evaluation of a market-based recommender system. Auton Agent Multi-Agent Syst 17, 251–269 (2008). https://doi.org/10.1007/s10458-008-9029-x

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  • DOI: https://doi.org/10.1007/s10458-008-9029-x

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