MBGR: Multi-Business Prediction for Generative Recommendation at Meituan
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917382638796800 |
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| author | Li, Changhao Yin, Junwei Zeng, Zhilin Kou, Senjie Wang, Shuli Chen, Wenshuai Zhu, Yinhua Wang, Haitao Wang, Xingxing |
| author_facet | Li, Changhao Yin, Junwei Zeng, Zhilin Kou, Senjie Wang, Shuli Chen, Wenshuai Zhu, Yinhua Wang, Haitao Wang, Xingxing |
| contents | Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space and employs the Next Token Prediction (NTP) framework to explore scaling laws. However, existing GR methods suffer from two critical issues: (1) a \textbf{seesaw phenomenon} in multi-business scenarios arises due to NTP's inability to capture complex cross-business behavioral patterns; and (2) a unified SID space causes \textbf{representation confusion} by failing to distinguish distinct semantic information across businesses. To address these issues, we propose Multi-Business Generative Recommendation (MBGR), the first GR framework tailored for multi-business scenarios. Our framework comprises three key components. First, we design a Business-aware semantic ID (BID) module that preserves semantic integrity via domain-aware tokenization. Then, we introduce a Multi-Business Prediction (MBP) structure to provide business-specific prediction capabilities. Furthermore, we develop a Label Dynamic Routing (LDR) module that transforms sparse multi-business labels into dense labels to further enhance the multi-business generation capability. Extensive offline and online experiments on Meituan's food delivery platform validate MBGR's effectiveness, and we have successfully deployed it in production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02684 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MBGR: Multi-Business Prediction for Generative Recommendation at Meituan Li, Changhao Yin, Junwei Zeng, Zhilin Kou, Senjie Wang, Shuli Chen, Wenshuai Zhu, Yinhua Wang, Haitao Wang, Xingxing Information Retrieval Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space and employs the Next Token Prediction (NTP) framework to explore scaling laws. However, existing GR methods suffer from two critical issues: (1) a \textbf{seesaw phenomenon} in multi-business scenarios arises due to NTP's inability to capture complex cross-business behavioral patterns; and (2) a unified SID space causes \textbf{representation confusion} by failing to distinguish distinct semantic information across businesses. To address these issues, we propose Multi-Business Generative Recommendation (MBGR), the first GR framework tailored for multi-business scenarios. Our framework comprises three key components. First, we design a Business-aware semantic ID (BID) module that preserves semantic integrity via domain-aware tokenization. Then, we introduce a Multi-Business Prediction (MBP) structure to provide business-specific prediction capabilities. Furthermore, we develop a Label Dynamic Routing (LDR) module that transforms sparse multi-business labels into dense labels to further enhance the multi-business generation capability. Extensive offline and online experiments on Meituan's food delivery platform validate MBGR's effectiveness, and we have successfully deployed it in production. |
| title | MBGR: Multi-Business Prediction for Generative Recommendation at Meituan |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2604.02684 |