We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point\r\nbased on a user�s Kansei through the interaction between the user and machine. However, especially in the domain of product\r\nrecommendations, theremay be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover\r\nmethod that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the\r\nlocations of the optimumarea by a clustering method and then searches for the maximumvalues of the area by a probabilisticmodel.\r\nTo confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an\r\nexperiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a\r\nset of pseudomultiple preferences.With this experiment, we proved that when there aremultiple preferences, the proposed method\r\nsearches faster andmore diversely than the conventional one.Thesecond experiment was a subjective experiment. This experiment\r\nshowed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences.
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