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A competitive information recommendation system and its rational recommendation method

Retrieval engines are widely used as a means for users to acquire information satisfying their requirements from the large amount of diversified Web information on the Internet. However, retrieval engines cannot be useful unless the requirements from the user are explicitly represented in terms of keywords. Consequently, information recommendation systems are attracting attention as a means of providing information which is likely to interest the user. This paper proposes a competitive information recommendation system, which recommends information to the user by competitive interaction of multiple agents, each having an individual character, with the user. In the competitive information recommendation system, if the agent simply recommends the information autonomously, information desired by the user may not be recommended, or many interactions may be needed before the user acquires the desired information. Therefore, this paper proposes a rational recommendation method in which the utility for the user that can be obtained from the recommended information is represented by a multi-attribute utility function, and the agent recommends only information desirable to the user while learning the weight for each attribute. Then the best profit strategy, in which the profit of the agent has priority, and the best learning strategy, in which learning of the utility to the user has priority, are presented as information recommendation strategies for the agent. An evaluation experiment shows that the best profit strategy is better. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(9): 74–84, 2007; Published online in Wiley InterScience (). DOI 10.1002sscj.10662