Information-Aware Long Sequence Compression for Sequential Recommendation

Abstract

Sequential recommendation (SR) aims to predict a user’s next interaction by modeling temporal dependencies in historical behavior sequences. However, modeling long sequences introduces two challenges: longer histories often include noisy interactions irrelevant to a user’s core interests, and increasing sequence length substantially raises computational cost while often degrading prediction accuracy due to noise accumulation. We present RDSR, a Rate–Distortion–based Sequential Recommendation framework grounded in a task-oriented rate-utility view. Instead of directly modeling full-length sequences, RDSR combines fixed-capacity token selection with VIB-based latent compression to retain task-relevant information under explicit rate control. This suppresses irrelevant interactions while preserving essential preference signals, effectively reducing sequence length and computational overhead. Extensive experiments show that RDSR improves the performance–efficiency trade-off across attention-, MLP-, and SSM-based SR backbones, with performance gains depending on backbone inductive bias. Our code and supplementary material are available at https://github.com/kangvic0615/Rate-Distortion-SR