GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Crossover method for interactive genetic algorithms to estimate multimodal preferences
Applied Computational Intelligence and Soft Computing
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The interactive genetic algorithm(iGA) is a method to obtain and predict a user's preference based on subjective evaluation of users, and it has been applied to many unimodal problems, such as designing clothes or fitting of hearing aids. On the other hand, we are interested in applying iGA to user's preferences, which can be described as a multimodal problem with equivalent fitness values at the peaks. For example, when iGA is applied to product recommendation on shopping sites, users have several types of preference trends at the same time in product selection. Hence, reflecting all the trends in product presentation leads to increased sales and consumer satisfaction. In this paper, we propose a new offspring generation method that enables efficient search even with multimodal user preferences by introducing clustering of selected individuals and generating offspring from each cluster. Furthermore, we perform a subjective experiment using an experimental iGA system for product recommendation to verify the efficiency of the proposed method. The results confirms that the proposed method enables offspring generation with consideration of multimodal preferences, and there is no negative influence on the performance of preference prediction by iGA.