提出GenRank架构，让推荐系统生成式排序更高效可靠。
Towards Large-scale Generative Ranking
- 用生成式架构替代传统排序，提升推荐效果。
- 线上实验显示用户满意度显著提升，资源消耗相当。
- 专为大规模工业场景设计，兼顾效果与效率。
生成式推荐近年来成为信息检索的有前景范式，但生成式排序系统在大规模工业场景中的有效性与可行性仍研究不足。本文以小红书探索页推荐系统为研究对象，针对其排序阶段进行分析。通过理论与实证研究发现，生成式架构本身是性能提升的关键，而非训练方式。为此，我们提出GenRank——一种新型生成式排序架构。在线A/B实验验证表明，相比现有生产系统，GenRank在几乎相同的计算资源下显著提升了用户满意度。
原文摘要 · Abstract (English)
Generative recommendation has recently emerged as a promising paradigm in information retrieval. However, generative ranking systems are still understudied, particularly with respect to their effectiveness and feasibility in large-scale industrial settings. This paper investigates this topic at the ranking stage of Xiaohongshu's Explore Feed, a recommender system that serves hundreds of millions of users. Specifically, we first examine how generative ranking outperforms current industrial recommenders. Through theoretical and empirical analyses, we find that the primary improvement in effectiveness stems from the generative architecture, rather than the training paradigm. To facilitate efficient deployment of generative ranking, we introduce GenRank, a novel generative architecture for ranking. We validate the effectiveness and efficiency of our solution through online A/B experiments. The results show that GenRank achieves significant improvements in user satisfaction with nearly equivalent computational resources compared to the existing production system.
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