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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.04180 |
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| _version_ | 1866913826058797056 |
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| author | Huang, Yanhua Chen, Yuqi Cao, Xiong Yang, Rui Qi, Mingliang Zhu, Yinghao Han, Qingchang Liu, Yaowei Liu, Zhaoyu Yao, Xuefeng Jia, Yuting Ma, Leilei Zhang, Yinqi Zhu, Taoyu Zhang, Liujie Chen, Lei Chen, Weihang Zhu, Min Xu, Ruiwen Zhang, Lei |
| author_facet | Huang, Yanhua Chen, Yuqi Cao, Xiong Yang, Rui Qi, Mingliang Zhu, Yinghao Han, Qingchang Liu, Yaowei Liu, Zhaoyu Yao, Xuefeng Jia, Yuting Ma, Leilei Zhang, Yinqi Zhu, Taoyu Zhang, Liujie Chen, Lei Chen, Weihang Zhu, Min Xu, Ruiwen Zhang, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04180 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Large-scale Generative Ranking Huang, Yanhua Chen, Yuqi Cao, Xiong Yang, Rui Qi, Mingliang Zhu, Yinghao Han, Qingchang Liu, Yaowei Liu, Zhaoyu Yao, Xuefeng Jia, Yuting Ma, Leilei Zhang, Yinqi Zhu, Taoyu Zhang, Liujie Chen, Lei Chen, Weihang Zhu, Min Xu, Ruiwen Zhang, Lei Information Retrieval 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. |
| title | Towards Large-scale Generative Ranking |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2505.04180 |