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Market-Based Recommender Systems: Learning Users’ Interests by Quality Classification

Market-Based Recommender Systems: Learning Users’ Interests by Quality Classification
Market-Based Recommender Systems: Learning Users’ Interests by Quality Classification
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. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents according to the users’ ratings of their suggestions. Moreover, we have shown this incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively, however, each agent needs to classify its recommendations into different internal quality levels, learn the users’ interests and adapt its bidding behaviour for the various internal quality 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 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.
119-133
Wei, Yan Zheng
a5d942b8-a744-482a-949b-a95b5a47cf68
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Wei, Yan Zheng
a5d942b8-a744-482a-949b-a95b5a47cf68
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Wei, Yan Zheng, Moreau, Luc and Jennings, N. R. (2004) Market-Based Recommender Systems: Learning Users’ Interests by Quality Classification. The Six International Workshop on Agent-Oriented Information Systems (AOIS-2004). pp. 119-133 .

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. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents according to the users’ ratings of their suggestions. Moreover, we have shown this incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively, however, each agent needs to classify its recommendations into different internal quality levels, learn the users’ interests and adapt its bidding behaviour for the various internal quality 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 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: July
Venue - Dates: The Six International Workshop on Agent-Oriented Information Systems (AOIS-2004), 2004-07-01
Organisations: Web & Internet Science, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 259484
URI: https://eprints.soton.ac.uk/id/eprint/259484
PURE UUID: 35a88a41-31b5-47a2-9d29-f5195af55b5b
ORCID for Luc Moreau: ORCID iD orcid.org/0000-0002-3494-120X

Catalogue record

Date deposited: 06 Sep 2004
Last modified: 06 Jun 2018 13:03

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Contributors

Author: Yan Zheng Wei
Author: Luc Moreau ORCID iD
Author: N. R. Jennings

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