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arXiv · 2601.19158

Accelerating Generative Recommendation via Simple Categorical User Sequence Compression

Qijiong Liu·Lu Fan·Zhongzhou Liu·Xiaoyu Dong·Yuankai Luo·Guoyuan An·Nuo Chen·Wei Guo·Yong Liu·Xiao-Ming Wu
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Abstract

Although generative recommenders demonstrate improved performance with longer sequences, their real-time deployment is hindered by substantial computational costs. To address this challenge, we propose a simple yet effective method for compressing long-term user histories by leveraging inherent item categorical features, thereby preserving user interests while enhancing efficiency. Experiments on two large-scale datasets demonstrate that, compared to the influential HSTU model, our approach achieves up to a 6x reduction in computational cost and up to 39% higher accuracy at comparable cost (i.e., similar sequence length).

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Qijiong Liu, Lu Fan, Zhongzhou Liu, Xiaoyu Dong, Yuankai Luo, Guoyuan An, Nuo Chen, Wei Guo, Yong Liu, Xiao-Ming Wu. 2026-01-27. Accelerating Generative Recommendation via Simple Categorical User Sequence Compression. https://arxiv.org/abs/2601.19158

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