Click-Through Rate Estimation Model Based on User Multi-Type Feedback Behavior Sequences
In recommendation systems,models for predicting click-through rates typically rely on a user's recent clicks as input.However,this approach can fall short in fully capturing user interests,limiting the model's accuracy.To address this issue,a new click-through rate estimation model based on User Multi-Type Feedback Behavior(UMFB)is developed,designed to handle various types of user feedback sequences.The UMFB model incorporates both implicit and explicit feedback sequences,enabling it to capture diverse user interest preferences.Given that implicit feedback sequences often contain significant noise,the study introduced an interest-denoising layer based on Fourier transform to reduce interference.Furthermore,to address the data sparsity issue in explicit feedback sequences,an interest enhancement layer based on contrastive learning is implemented to improve modeling accuracy.Finally,a personalized interest fusion layer is utilized to effectively model user preferences.To evaluate the effectiveness of the UMFB model,extensive experiments are conducted on the KuaiRand-Pure and KuaiRand-1K datasets in the context of short video recommendations.The results demonstrated that the UMFB model significantly outperformed other state-of-the-art baseline models,with an Area Under the receiver operator characteristic Curve(AUC)improvement of 1.07 and 0.91 percentage points on the respective real datasets.
国家科技期刊平台
NSTL
万方数据
