Market-Based 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, we aims to the principled design and the development of such a marketplace (including the auction protocol, the reward mechanism and the bidding strategies of the component recommender agents) and evaluates the market's capability to effectively coordinate multiple methods. Through analysis, we show that our market is capable of shortlisting recommendations in decreasing order of user perceived quality and correlating the individual agent's internal quality rating to the user's perceived quality.
Type: Normal Research Project
Research Group: Intelligence, Agents, Multimedia Group
Themes: Decentralised Information Systems, E-Business Technologies, Web Science
Dates: 1st March 2002 to 1st March 2005
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