Recent advancements in autoregressive Large Language Models (LLMs) have achieved significant milestones, largely attributed to their scalability, often referred to as the "scaling law". Inspired by these achievements, there has been a growing interest in adapting LLMs for Recommendation Systems (RecSys) by reformulating RecSys tasks into generative problems. However, these End-to-End Generative Recommendation (E2E-GR) methods tend to prioritize idealized goals, often at the expense of the practical advantages offered by traditional Deep Learning based Recommendation Models (DLRMs) in terms of in features, architecture, and practices. This disparity between idealized goals and practical needs introduces several challenges and limitations, locking the scaling law in industrial RecSys. In this paper, we introduce a large user model (LUM) that addresses these limitations through a three-step paradigm, designed to meet the stringent requirements of industrial settings while unlocking the potential for scalable recommendations. Our extensive experimental evaluations demonstrate that LUM outperforms both state-of-the-art DLRMs and E2E-GR approaches. Notably, LUM exhibits excellent scalability, with performance improvements observed as the model scales up to 7 billion parameters. Additionally, we have successfully deployed LUM in an industrial application, where it achieved significant gains in an A/B test, further validating its effectiveness and practicality.

翻译：近年来，自回归大语言模型（LLMs）取得了重大进展，这主要归功于其可扩展性，通常被称为“缩放定律”。受这些成就的启发，通过将推荐系统（RecSys）任务重新表述为生成问题，将LLMs应用于推荐系统的兴趣日益增长。然而，这些端到端生成式推荐（E2E-GR）方法往往优先考虑理想化的目标，通常以牺牲传统基于深度学习的推荐模型（DLRMs）在特征、架构和实践方面提供的实际优势为代价。理想化目标与实际需求之间的这种差异带来了若干挑战和限制，锁定了工业推荐系统中的缩放定律。本文介绍了一种大规模用户模型（LUM），它通过一个三步范式解决了这些限制，旨在满足工业环境的严格要求，同时释放可扩展推荐的潜力。我们广泛的实验评估表明，LUM的性能优于最先进的DLRMs和E2E-GR方法。值得注意的是，LUM表现出卓越的可扩展性，当模型规模扩展到70亿参数时，观察到了性能提升。此外，我们已成功将LUM部署在一个工业应用中，在A/B测试中取得了显著收益，进一步验证了其有效性和实用性。