research-article

Learning Users' Interests by Quality Classification in Market-Based Recommender Systems

Authors:

Nicholas R. JenningsAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 17, Issue 12

Pages 1678 - 1688

https://doi.org/10.1109/TKDE.2005.200

Published: 01 December 2005 Publication History

Abstract

Recommender systems are widely used to cope with the problem of information overload and, to date, 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. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users' ratings of their suggestions. Moreover, we have theoretically shown how our system incites the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users' interests for these different levels, and then adapt its bidding behavior for the various levels accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users' interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations.

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Cited By

Wang HYeung D(2020)A Survey on Bayesian Deep LearningACM Computing Surveys10.1145/340938353:5(1-37)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3409383
Enríquez JMorales-Trujillo LCalle-Alonso FDomínguez-Mayo FLucas-Rodríguez JIribarne L(2019)Recommendation and Classification SystemsScientific Programming10.1155/2019/80439052019Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1155/2019/8043905
Barczyszyn GCamenar LNascimento DKozievitch NSilva RAlmeida LSanti JMinetto R(2018)A Collaborative System for Suitable Wheelchair Route PlanningACM Transactions on Accessible Computing10.1145/323718611:3(1-26)Online publication date: 28-Aug-2018
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Index Terms

Learning Users' Interests by Quality Classification in Market-Based Recommender Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
  2. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 17, Issue 12

December 2005

144 pages

ISSN:1041-4347

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Copyright © 2005.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 December 2005

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Cited By

Wang HYeung D(2020)A Survey on Bayesian Deep LearningACM Computing Surveys10.1145/340938353:5(1-37)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3409383
Enríquez JMorales-Trujillo LCalle-Alonso FDomínguez-Mayo FLucas-Rodríguez JIribarne L(2019)Recommendation and Classification SystemsScientific Programming10.1155/2019/80439052019Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1155/2019/8043905
Barczyszyn GCamenar LNascimento DKozievitch NSilva RAlmeida LSanti JMinetto R(2018)A Collaborative System for Suitable Wheelchair Route PlanningACM Transactions on Accessible Computing10.1145/323718611:3(1-26)Online publication date: 28-Aug-2018
https://dl.acm.org/doi/10.1145/3237186
Hao Wang Wu-Jun Li (2015)Relational Collaborative Topic Regression for Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2014.236578927:5(1343-1355)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1109/TKDE.2014.2365789
Forsati RMoayedikia AShamsfard M(2015)An effective Web page recommender using binary data clusteringInformation Retrieval10.1007/s10791-015-9252-418:3(167-214)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s10791-015-9252-4
Johar MMookerjee VSarkar S(2014)Selling vs. ProfilingInformation Systems Research10.5555/2895238.289524325:2(285-306)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.5555/2895238.2895243
Park DKim HChoi IKim J(2012)A literature review and classification of recommender systems researchExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.02.03839:11(10059-10072)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.02.038
Mei TYang BHua XLi S(2011)Contextual Video Recommendation by Multimodal Relevance and User FeedbackACM Transactions on Information Systems10.1145/1961209.196121329:2(1-24)Online publication date: 1-Apr-2011
https://dl.acm.org/doi/10.1145/1961209.1961213
Even AKolodner YVarshavsky R(2010)Designing business-intelligence tools with value-driven recommendationsProceedings of the 5th international conference on Global Perspectives on Design Science Research10.1007/978-3-642-13335-0_20(286-301)Online publication date: 4-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13335-0_20
Wei YJennings NMoreau LHall W(2008)User evaluation of a market-based recommender systemAutonomous Agents and Multi-Agent Systems10.1007/s10458-008-9029-x17:2(251-269)Online publication date: 1-Oct-2008
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