Fab: content-based, collaborative recommendation
Communications of the ACM
A scalable comparison-shopping agent for the World-Wide Web
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Adding Life-Like Synthetic Characters to the Web
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
Interactive Integration of Information Agents on the Web
CIA '01 Proceedings of the 5th International Workshop on Cooperative Information Agents V
ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Automatic Chat Generation of Emotional Entertainment Characters Using News Information
ICEC '09 Proceedings of the 8th International Conference on Entertainment Computing
Toward web information integration on 3d virtual space
ICEC'05 Proceedings of the 4th international conference on Entertainment Computing
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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 broker-mediated 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.