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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.11235 |
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| _version_ | 1866911444302299136 |
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| author | Song, Xin Guan, Zhilin Han, Ruidong Tang, Binghao Chen, Tianwen Li, Bing Li, Zihao Zhang, Han Jiang, Fei Wang, Qing Xu, Zikang Li, Fengyi Jing, Chunzhen Yu, Lei Lin, Wei |
| author_facet | Song, Xin Guan, Zhilin Han, Ruidong Tang, Binghao Chen, Tianwen Li, Bing Li, Zihao Zhang, Han Jiang, Fei Wang, Qing Xu, Zikang Li, Fengyi Jing, Chunzhen Yu, Lei Lin, Wei |
| contents | Industrial recommendation systems typically involve multiple scenarios, yet existing cross-domain (CDR) and multi-scenario (MSR) methods often require prohibitive resources and strict input alignment, limiting their extensibility. We propose MTFM (Meituan Foundation Model for Recommendation), a transformer-based framework that addresses these challenges. Instead of pre-aligning inputs, MTFM transforms cross-domain data into heterogeneous tokens, capturing multi-scenario knowledge in an alignment-free manner. To enhance efficiency, we first introduce a multi-scenario user-level sample aggregation that significantly enhances training throughput by reducing the total number of instances. We further integrate Grouped-Query Attention and a customized Hybrid Target Attention to minimize memory usage and computational complexity. Furthermore, we implement various system-level optimizations, such as kernel fusion and the elimination of CPU-GPU blocking, to further enhance both training and inference throughput. Offline and online experiments validate the effectiveness of MTFM, demonstrating that significant performance gains are achieved by scaling both model capacity and multi-scenario training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11235 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan Song, Xin Guan, Zhilin Han, Ruidong Tang, Binghao Chen, Tianwen Li, Bing Li, Zihao Zhang, Han Jiang, Fei Wang, Qing Xu, Zikang Li, Fengyi Jing, Chunzhen Yu, Lei Lin, Wei Information Retrieval Industrial recommendation systems typically involve multiple scenarios, yet existing cross-domain (CDR) and multi-scenario (MSR) methods often require prohibitive resources and strict input alignment, limiting their extensibility. We propose MTFM (Meituan Foundation Model for Recommendation), a transformer-based framework that addresses these challenges. Instead of pre-aligning inputs, MTFM transforms cross-domain data into heterogeneous tokens, capturing multi-scenario knowledge in an alignment-free manner. To enhance efficiency, we first introduce a multi-scenario user-level sample aggregation that significantly enhances training throughput by reducing the total number of instances. We further integrate Grouped-Query Attention and a customized Hybrid Target Attention to minimize memory usage and computational complexity. Furthermore, we implement various system-level optimizations, such as kernel fusion and the elimination of CPU-GPU blocking, to further enhance both training and inference throughput. Offline and online experiments validate the effectiveness of MTFM, demonstrating that significant performance gains are achieved by scaling both model capacity and multi-scenario training data. |
| title | MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.11235 |