Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation

• URL: http://arxiv.org/abs/2511.06285v3
• Date: Fri, 14 Nov 2025 02:02:16 GMT
• Title: Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation
• Authors: Peng He, Yao Liu, Yanglei Gan, Run Lin, Tingting Dai, Qiao Liu, Xuexin Li,
• Abstract summary: We propose FreqRec, a Frequency-Enhanced Dual-Path Network for sequential Recommendation.<n>FreqRec captures inter-session and intra-session behaviors via a learnable Frequency-domain Multi-layer Perceptrons.<n>It is optimized under a composite objective that combines cross entropy with a frequency-domain consistency loss.
• Score: 13.302194364808514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommendation (SR) aims to predict a user's next item preference by modeling historical interaction sequences. Recent advances often integrate frequency-domain modules to compensate for self-attention's low-pass nature by restoring the high-frequency signals critical for personalized recommendations. Nevertheless, existing frequency-aware solutions process each session in isolation and optimize exclusively with time-domain objectives. Consequently, they overlook cross-session spectral dependencies and fail to enforce alignment between predicted and actual spectral signatures, leaving valuable frequency information under-exploited. To this end, we propose FreqRec, a Frequency-Enhanced Dual-Path Network for sequential Recommendation that jointly captures inter-session and intra-session behaviors via a learnable Frequency-domain Multi-layer Perceptrons. Moreover, FreqRec is optimized under a composite objective that combines cross entropy with a frequency-domain consistency loss, explicitly aligning predicted and true spectral signatures. Extensive experiments on three benchmarks show that FreqRec surpasses strong baselines and remains robust under data sparsity and noisy-log conditions.

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