用物品类别压缩用户序列，提速降耗还更准
Accelerating Generative Recommendation via Simple Categorical User Sequence Compression
- 利用物品类别特征压缩用户历史序列
- 计算成本降低6倍，精度提升39%
- 适合需要实时推荐的场景
尽管生成式推荐器在使用长序列时表现更优，但其实时部署受制于高昂的计算开销。为此，我们提出一种简单有效的方法，通过利用物品固有的类别特征压缩长期用户行为序列，既保留用户兴趣又提升效率。在两个大规模数据集上的实验表明，相比具有影响力的HSTU模型，该方法在相似序列长度下，计算成本最多降低6倍，精度最高提升39%。
原文摘要 · Abstract (English)
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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