Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences
Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences
复制标题
交互式遗传算法估计多模态偏好的交叉方法
DOI：
10.1155/2013/302573
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发表时间：
2013
影响因子：
2.9
通讯作者：
Mitsunori Miki and Tomoyuki Hiroyasu
中科院分区：
文献类型：
--
作者：
Misato Tanaka;Yasunari Sasaki;Mitsunori Miki and Tomoyuki Hiroyasu
我们应用交互式遗传算法（iGA）来生成产品推荐。 iGA 通过用户与机器之间的交互，根据用户的感性搜索单个最佳点。然而，特别是在产品推荐领域，可能存在许多最佳点。因此，本研究的目的是开发一种新的 iGA 交叉方法，该方法可以同时搜索多个用户偏好的多个最佳点。该方法通过聚类方法估计最佳区域的位置，然后通过概率模型搜索该区域的最大值。为了证实该方法的有效性，进行了两个实验。在第一个实验中，伪用户操作了一个实现所提出的方法和传统方法的实验系统，并使用一组伪多重偏好来评估所获得的解决方案。通过这个实验，我们证明了当存在多个偏好时，所提出的方法比传统方法搜索更快、更多样化。第二个实验是主观实验。该实验表明，当受试者有多个偏好时，所提出的方法能够同时搜索更多偏好。
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.