用时变卷积滤波器替代注意力机制，提升序列推荐效果。
TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
- 引入时变卷积滤波器捕捉用户行为的时间位置变化特征。
- 在6个公开数据集上平均性能超越基线7.49%。
- 无需自注意力机制，计算更高效，适合实时推荐场景。
近期，卷积滤波器因其捕捉局部序列模式的能力被广泛应用于序列推荐。然而，多数模型仍需结合自注意力机制，因为固定卷积核难以捕捉全局交互关系。本文提出时间变卷积滤波器（TV-Rec），受图信号处理启发，通过时变图滤波器捕捉用户序列中位置相关的时序变化。通过用时变滤波器替代固定卷积核与自注意力机制，TV-Rec显著提升表达能力，更好地建模用户行为的复杂交互模式。该设计不仅无需自注意力，还降低计算开销，加速推理过程。在六个公开基准数据集上的实验表明，TV-Rec平均优于现有最优方法7.49%。
原文摘要 · Abstract (English)
Recently, convolutional filters have been increasingly adopted in sequential recommendation for their ability to capture local sequential patterns. However, most of these models complement convolutional filters with self-attention. This is because convolutional filters alone, generally fixed filters, struggle to capture global interactions necessary for accurate recommendation. We propose Time-Variant Convolutional Filters for Sequential Recommendation (TV-Rec), a model inspired by graph signal processing, where time-variant graph filters capture position-dependent temporal variations in user sequences. By replacing both fixed kernels and self-attention with time-variant filters, TV-Rec achieves higher expressive power and better captures complex interaction patterns in user behavior. This design not only eliminates the need for self-attention but also reduces computation while accelerating inference. Extensive experiments on six public benchmarks show that TV-Rec outperforms state-of-the-art baselines by an average of 7.49%.
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