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

TL;DR

TV-Rec introduces time-variant convolutional filters inspired by graph signal processing to improve sequential recommendation by capturing complex, position-dependent user behavior patterns more efficiently than traditional methods.

Contribution

The paper proposes a novel time-variant convolutional filter model for sequential recommendation that replaces fixed kernels and self-attention, enhancing expressiveness and computational efficiency.

Findings

Outperforms state-of-the-art models by 7.49% on average.

Reduces computation and accelerates inference compared to existing methods.

Effectively captures complex, position-dependent user interaction patterns.

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%.