TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation

• URL: http://arxiv.org/abs/2510.25259v1
• Date: Wed, 29 Oct 2025 08:14:03 GMT
• Title: TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
• Authors: Yehjin Shin, Jeongwhan Choi, Seojin Kim, Noseong Park,
• Abstract summary: We propose Time-Variant Convolutional Filters for Sequential Recommendation (TV-Rec), a model inspired by graph signal processing.<n>TV-Rec achieves higher expressive power and better captures complex interaction patterns in user behavior.<n>Extensive experiments on six public benchmarks show that TV-Rec outperforms state-of-the-art baselines by an average of 7.49%.
• Score: 35.07451950291286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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