%0 Journal Article
%T Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences
%A Misato Tanaka
%A Yasunari Sasaki
%A Mitsunori Miki
%A Tomoyuki Hiroyasu
%J Applied Computational Intelligence and Soft Computing
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/302573
%X We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a userĄ¯s Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, there may be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover method that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the locations of the optimum area by a clustering method and then searches for the maximum values of the area by a probabilistic model. To confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an experiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a set of pseudomultiple preferences. With this experiment, we proved that when there are multiple preferences, the proposed method searches faster and more diversely than the conventional one. The second experiment was a subjective experiment. This experiment showed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences. 1. Introduction At present, in the e-commerce of business-to-consumer, product recommendation is very important. The number of products sold on online shopping sites is increasing. Moreover, to improve sales, each site uses search techniques or recommendations to display its products. Because search techniques take into account user direct input, they return products that users expect. In contrast, because product recommendation techniques use action logs of users to analyze their needs, they display products that users do not expect. At present, the main recommendation techniques are contents based filtering [1, 2] and collaborative filtering [3¨C5]. The former recommends products by matching a userĄ¯s profile and action logs with features of products, whereas the latter recommends products on the basis of frequency they are bought at the same time. Therefore, we aim at displaying products that fit a personal Kansei model. Kansei is a Japanese term that relates to human characteristics such as sensibility, perception, affection, or subjectivity. We assume that human Kansei is modeled as a function. The input parameters of the function are the features of objects or the factors of environment and the output parameters are subjective evaluations such as preference or impression. This internal model in human Kansei
%U http://www.hindawi.com/journals/acisc/2013/302573/