A Competitive Information Recommendation System and Its Behavior

Information recommendation systems draw attention of practitioners in B-to-C electronic commerce. In an independent recommendation system such as in www.amazon.com, a user cannot compare the recommended item with ones from other information sources. In a brokermediated recommendation system such as in www.dealtime.com, the broker takes the initiative of recommendation, and the information provider cannot recommend its item directly to the user. In this paper, we propose a competitive information recommendation system consisting of multiple animated agents that recommend their items competitively, and discuss the advantages through showing a prototype developed for restaurant recommendation. Each agent recommends restaurants from its own point of view and the user tells good or bad about them. In our competitive information recommendation system, the user can compare items recommended from multiple agents, and the information providers can recommend their items directly to the user through its animated agent. We also show that the competitive nature affects the output depending on the number of participating agents.