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.
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