Personalized differential evolutionary algorithm enhanced by surrogate nodel and Kalman filter deviation estimation
SUN Xiao-yan
LI Shuai
JIN Yao-chu
Abstract：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.
Keywords：personalized searchdifferential evolutionary algorithmKalman filtersurrogate modeldeviation estima-tion
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：11( 2386-2396 )