MTFM：一种可扩展且无对齐的工业推荐基础模型
MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan
AI总结：
MTFM是一种基于Transformer的工业推荐基础模型，通过异构标记和多场景样本聚合提升效率，实现无对齐的多场景推荐效果。
摘要：
工业推荐系统通常涉及多个场景，但现有跨域（CDR）和多场景（MSR）方法往往需要巨大的资源和严格的输入对齐，限制了其扩展性。我们提出了MTFM（美团推荐基础模型），一种基于Transformer的框架，解决了这些挑战。不同于预先对齐输入，MTFM将跨域数据转换为异构标记，以无对齐的方式捕捉多场景知识。为了提高效率，我们首先引入了多场景用户级样本聚合，通过减少总实例数显著提高了训练吞吐量。我们进一步集成了分组查询注意力和定制化的混合目标注意力，以最小化内存使用和计算复杂性。此外，我们实现了各种系统级优化，如内核融合和消除CPU-GPU阻塞，以进一步提高训练和推理吞吐量。离线和在线实验验证了MTFM的有效性，证明通过扩大模型容量和多场景训练数据，实现了显著的性能提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
