| 引用本文: | 孙晓燕,李帅,金耀初.代理模型和Kalman滤波偏差估计增强的个性化差分进化算法[J].控制理论与应用,2025,42(11):2386~2396.[点击复制] |
| SUN Xiao-yan,LI Shuai,JIN Yao-chu.Personalized differential evolutionary algorithm enhanced by surrogate nodel and Kalman filter deviation estimation[J].Control Theory & Applications,2025,42(11):2386~2396.[点击复制] |
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| 代理模型和Kalman滤波偏差估计增强的个性化差分进化算法 |
| Personalized differential evolutionary algorithm enhanced by surrogate nodel and Kalman filter deviation estimation |
| 摘要点击 2424 全文点击 113 投稿时间：2025-05-19 修订日期：2025-10-22 |
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| DOI编号 10.7641/CTA.2025.50210 |
| 2025,42(11):2386-2396 |
| 中文关键词 个性化搜索 差分进化算法 Kalman滤波器 代理模型 偏差估计 |
| 英文关键词 personalized search differential evolutionary algorithm Kalman filter surrogate model deviation estima tion |
| 基金项目 国家自然科学基金项目(61876184)资助. |
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| 中文摘要 |
| 基于用户交互的进化优化算法可有效提高个性化推荐的性能,但已有研究忽略了编码个体与解码样本间
的偏差,往往导致算法搜索方向出现较大偏离,搜索效率低;此外,用户交互评价的定量化表示也是较大挑战.针对
此, 本文提出了融合Kalman滤波偏差估计和代理模型的个性化差分进化算法.首先,构建了基于用户评价、商品属
性等的深度信念网络代理模型,实现对用户交互的定量评价;然后,设计Kalman滤波偏差估计器,跟踪进化过程中
基因型和表现型之间的偏差,并基于该偏差设计差分进化算子,改变种群分布并引导搜索方向;最后,将该算法应用
于亚马逊个性化搜索数据集,验证了其有效性. |
| 英文摘要 |
| User interaction-based evolutionary optimization can effectively improve the performance of personalized
recommendation. However, existing studies have overlooked the deviation between the encoded individuals and the de
coded candidates, often resulting in a significant deviation in the search direction and low search efficiency. Moreover, the
quantitative representation of user interaction evaluation is also a major challenge. To address this, this paper proposes a
personalized differential evolution algorithm that integrates Kalman filter deviation estimation and surrogate models. First
ly, a deep belief network trained with user evaluation and product attributes is constructed to achieve quantitative evaluation
of user interactions. Then, a Kalman filter estimator is designed to track the deviation between genotypes and phenotypes
during the evolution process, and a differential evolution operator is designed based on this deviation to change the popula
tion distribution and guide the search direction. Finally, this algorithm is applied to the Amazon personalized search dataset
to verify its effectiveness. |
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