The University of Southampton
University of Southampton Institutional Repository

A Market-Based Approach to Recommender Systems

A Market-Based Approach to Recommender Systems
A Market-Based Approach to Recommender Systems
Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have been developed. However, it has been shown that no one technique is best for all users in all situations. Thus, we believe that effective recommender systems should incorporate a wide variety of such techniques and that some form of overarching framework should be put in place to coordinate the various recommendations so that only the best of them (from whatever source) are presented to the user. To this end, we show that a marketplace, in which the various recommendation methods compete to offer their recommendations to the user, can be used in this role. Specifically, our research is concerned with the principled design of such a marketplace (including the auction protocol, the reward mechanism and the bidding strategies of the individual recommender agents) and its evaluation in terms of how it can effectively coordinate multiple methods. In addition to the market mechanisms, a reinforcement learning strategy is developed to assist the individual recommender agents' bidding behaviour so as to learn the users' interests and still maximize their revenue. Finally, we evaluate our approach with a real market-based recommender system that is composed of a number of typical recommendation methods and that is evaluated with real users. The evaluation results show that our approach is indeed an effective means of coordinating multiple different recommendation methods in one single system and is an effective way of dealing with the problem of information overload.
Recommender System, Information Filtering, Market-based Design, Auctions, Reinforcement Learning
Wei, Yan Zheng
a5d942b8-a744-482a-949b-a95b5a47cf68
Wei, Yan Zheng
a5d942b8-a744-482a-949b-a95b5a47cf68

Wei, Yan Zheng (2005) A Market-Based Approach to Recommender Systems. University of Southampton, Electronics and Computer Science, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have been developed. However, it has been shown that no one technique is best for all users in all situations. Thus, we believe that effective recommender systems should incorporate a wide variety of such techniques and that some form of overarching framework should be put in place to coordinate the various recommendations so that only the best of them (from whatever source) are presented to the user. To this end, we show that a marketplace, in which the various recommendation methods compete to offer their recommendations to the user, can be used in this role. Specifically, our research is concerned with the principled design of such a marketplace (including the auction protocol, the reward mechanism and the bidding strategies of the individual recommender agents) and its evaluation in terms of how it can effectively coordinate multiple methods. In addition to the market mechanisms, a reinforcement learning strategy is developed to assist the individual recommender agents' bidding behaviour so as to learn the users' interests and still maximize their revenue. Finally, we evaluate our approach with a real market-based recommender system that is composed of a number of typical recommendation methods and that is evaluated with real users. The evaluation results show that our approach is indeed an effective means of coordinating multiple different recommendation methods in one single system and is an effective way of dealing with the problem of information overload.

Text
wei-phd-thesis.pdf - Other
Download (1MB)

More information

Published date: March 2005
Keywords: Recommender System, Information Filtering, Market-based Design, Auctions, Reinforcement Learning
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 260692
URI: http://eprints.soton.ac.uk/id/eprint/260692
PURE UUID: ee1a9aff-61e6-4447-9ec2-91a1df680052

Catalogue record

Date deposited: 18 Mar 2005
Last modified: 16 Dec 2019 20:44

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×