Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences
We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’s Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, there may be numerous optimum point...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2013/302573 |
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| author | Misato Tanaka Yasunari Sasaki Mitsunori Miki Tomoyuki Hiroyasu |
| author_facet | Misato Tanaka Yasunari Sasaki Mitsunori Miki Tomoyuki Hiroyasu |
| author_sort | Misato Tanaka |
| collection | DOAJ |
| description | We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’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. |
| format | Article |
| id | doaj-art-d7fb4de998954b2c978a7c0e8356c441 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-d7fb4de998954b2c978a7c0e8356c4412025-02-03T01:13:01ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322013-01-01201310.1155/2013/302573302573Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal PreferencesMisato Tanaka0Yasunari Sasaki1Mitsunori Miki2Tomoyuki Hiroyasu3Graduate School of Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, JapanKanazawa Seiryo University Women’s Junior College, 10-1 Ushi, Gosho-machi, Kanazawa-shi, Ishikawa 920-8620, JapanFaculty of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, JapanFaculty of Life and Medical Sciences, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, JapanWe apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’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.http://dx.doi.org/10.1155/2013/302573 |
| spellingShingle | Misato Tanaka Yasunari Sasaki Mitsunori Miki Tomoyuki Hiroyasu Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences Applied Computational Intelligence and Soft Computing |
| title | Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences |
| title_full | Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences |
| title_fullStr | Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences |
| title_full_unstemmed | Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences |
| title_short | Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences |
| title_sort | crossover method for interactive genetic algorithms to estimate multimodal preferences |
| url | http://dx.doi.org/10.1155/2013/302573 |
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