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).

翻译：尽管生成式推荐系统在更长的用户序列上展现出性能提升，但其实际部署受到高计算成本的制约。为解决这一问题，我们提出了一种简单而有效的方法，通过利用物品固有的类别特征来压缩长期用户历史记录，从而在保持用户兴趣的同时提升效率。在两个大规模数据集上的实验表明，与具有影响力的HSTU模型相比，我们的方法在计算成本上最高可降低6倍，并在相近成本（即相似序列长度）下实现最高39%的准确率提升。