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A Cross Attention Transformer-Mixed Feedback Video Recommendation Algorithm Based on DIEN

作     者:Jianwei Zhang Zhishang Zhao Zengyu Cai Yuan Feng Liang Zhu Yahui Sun 

作者机构:College of Software EngineeringZhengzhou University of Light IndustryZhengzhou450000China Faculty of Information EngineeringXuchang Vocational Technical CollegeXuchang461000China College of Computer Science and TechnologyZhengzhou University of Light IndustryZhengzhou450000China College of Electronics and InformationZhengzhou University of Light IndustryZhengzhou450000China 

出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))

年 卷 期:2025年第82卷第1期

页      面:977-996页

核心收录:

学科分类:0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学] 

基  金:supported by National Natural Science Foundation of China(62072416) Key Research and Development Special Project of Henan Province(221111210500) Key TechnologiesR&DProgram of Henan rovince(232102211053,242102211071) 

主  题:Video recommendation user interest cross-attention transformer 

摘      要:The rapid development of short video platforms poses new challenges for traditional recommendation *** systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term ***,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior ***,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their *** paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)*** study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention ***,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user *** results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance *** advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.

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