# Towards Large-scale Generative Ranking

arXiv: https://arxiv.org/abs/2505.04180 · HTML: https://mlsanity.com/paper/2505.04180/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2025.

## Built on by (most cited first, 10 of 10)

- [PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications](https://mlsanity.com/paper/2505.07203/) (2025)
- [Reasoning over Semantic IDs Enhances Generative Recommendation](https://mlsanity.com/paper/2603.23183/) (2026)
- [MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation](https://mlsanity.com/paper/2508.15281/) (2025)
- [SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation](https://mlsanity.com/paper/2509.21777/) (2025)
- [A Metric for MLLM Alignment in Large-scale Recommendation](https://mlsanity.com/paper/2508.04963/) (2025)
- [Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders](https://mlsanity.com/paper/2601.05588/) (2026)
- [Learning Variable-Length Tokenization for Generative Recommendation](https://mlsanity.com/paper/2605.17779/) (2026)
- [Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation](https://mlsanity.com/paper/2604.20861/) (2026)
- [When KV Meets Embeddings: Dynamic GPU Memory Allocation for Accelerating Generative Recommender Serving](https://mlsanity.com/paper/2605.04450/) (2026)
- [Population-Level Generative Modeling for Ranking Data](https://mlsanity.com/paper/2608.08422/) (2026)

## Abstract (arXiv)

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.