A market-based approach to recommender systems

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, this article presents the principled design of such a marketplace (including the auction protocol, the reward mechanism, and the bidding strategies of the individual recommendation agents) and evaluates the market's capability to effectively coordinate multiple methods. Through analysis and simulation, we show that our market is capable of shortlisting recommendations in decreasing order of user perceived quality and of correlating the individual agent's internal quality rating to the user's perceived quality.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.m Miscellaneous

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering
      H.3.4 Systems and Software
          Subjects: Performance evaluation (efficiency and effectiveness)

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Multiagent systems

General Terms:
Algorithms, Design, Economics

Keywords:
Recommender systems, auctions, marketplace