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Learning users' interests by quality classification in market-based recommender systems

Learning users' interests by quality classification in market-based recommender systems
Learning users' interests by quality classification in market-based recommender systems
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 incentivises 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 behaviour 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.
Information Filtering, Machine learning, Recommender Systems, Markets
1678-1688
Wei, Y.Z.
6bb88665-8be6-4c8e-b87f-dcbc4a399035
Moreau, L.
033c63dd-3fe9-4040-849f-dfccbe0406f8
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Wei, Y.Z.
6bb88665-8be6-4c8e-b87f-dcbc4a399035
Moreau, L.
033c63dd-3fe9-4040-849f-dfccbe0406f8
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Wei, Y.Z., Moreau, L. and Jennings, N. R. (2005) Learning users' interests by quality classification in market-based recommender systems. IEEE Trans on Knowledge and Data Engineering, 17 (12), 1678-1688.

Record type: Article

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 incentivises 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 behaviour 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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More information

Published date: 2005
Keywords: Information Filtering, Machine learning, Recommender Systems, Markets
Organisations: Web & Internet Science, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 260826
URI: https://eprints.soton.ac.uk/id/eprint/260826
PURE UUID: 9a6919de-867d-4398-b5c2-d3f3b2df0d59
ORCID for L. Moreau: ORCID iD orcid.org/0000-0002-3494-120X

Catalogue record

Date deposited: 29 Apr 2005
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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