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Learning users' interests in a market-based recommender system

Learning users' interests in a market-based recommender system
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 turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations.
833-840
Wei, Y. Z.
61929f07-075b-4792-9204-3c69aa4c16f6
Moreau, L.
033c63dd-3fe9-4040-849f-dfccbe0406f8
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Wei, Y. Z.
61929f07-075b-4792-9204-3c69aa4c16f6
Moreau, L.
033c63dd-3fe9-4040-849f-dfccbe0406f8
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Wei, Y. Z., Moreau, L. and Jennings, N. R. (2004) Learning users' interests in a market-based recommender system. 5th International Conference on Intelligent Data Engineering and Automated Learning, United Kingdom. pp. 833-840 .

Record type: Conference or Workshop Item (Paper)

Abstract

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 turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations.

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More information

Published date: 2004
Additional Information: Event Dates: 2004
Venue - Dates: 5th International Conference on Intelligent Data Engineering and Automated Learning, United Kingdom, 2004-01-01
Organisations: Web & Internet Science, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 259568
URI: https://eprints.soton.ac.uk/id/eprint/259568
PURE UUID: 8717348f-c4e2-49d2-b708-c0a957d4faf6
ORCID for L. Moreau: ORCID iD orcid.org/0000-0002-3494-120X

Catalogue record

Date deposited: 30 Jul 2004
Last modified: 06 Jun 2018 13:03

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Contributors

Author: Y. Z. Wei
Author: L. Moreau ORCID iD
Author: N. R. Jennings

University divisions

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