HyTiFRec:Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation  

作　　者：Dawei Qiu Peng Wu Xiaoming Zhang Renjie Xu 

机构地区：[1]School of Information Science and Engineering,Zhejiang Sci-Tech University,Hangzhou,310018,China [2]Department of Vehicle Engineering,Army Academy of Armored Forces,Beijing,100072,China [3]Performance and Training Center,Army Academy of Armored Forces,Beijing,100072,China

出　　处：《Computers, Materials & Continua》2025年第5期1753-1769,共17页计算机、材料和连续体(英文)

基　　金：supported by a grant from the Natural Science Foundation of Zhejiang Province under Grant LY21F010016.

摘　　要：Recently,many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences.However,these methods tend to focus more on low-frequency information while neglecting highfrequency information,which makes them ineffective in balancing users’long-and short-term preferences.At the same time,manymethods overlook the potential of frequency domainmethods,ignoring their efficiency in processing frequency information.To overcome this limitation,we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation,namely HyTiFRec.Specifically,we design two hybrid filter modules:the learnable hybrid filter(LHF)and the window hybrid filter(WHF).We combine these with the Efficient Attention(EA)module to form the dual-branch structure to replace the self-attention components in Transformers.The EAmodule is used to extract sequential and global information.The LHF andWHF modules balance the proportion of different frequency bands,with LHF globally modulating the spectrum in the frequency domain and WHF retaining frequency components within specific local frequency bands.Furthermore,we use a time domain residual information addition operation in the hybrid filter module,which reduces information loss and further facilitates the hybrid of time-frequency methods.Extensive experiments on five widely-used real-world datasets show that our proposed method surpasses state-of-the-art methods.

关 键 词：Sequential recommendation frequency domain efficient attention 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]