Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for industrial use. However, the expansion of network parameters in traditional recommendation models has plateaued at tens of millions, limiting further benefits from increased embedding parameters. Inspired by the success of large language models (LLMs), a new approach has emerged that scales network parameters using innovative structures, enabling continued performance improvements. A significant development in this area is Meta's generative recommendation model HSTU, which illustrates the scaling laws of recommendation systems by expanding parameters to thousands of billions. This new paradigm has achieved substantial performance gains in online experiments. In this paper, we aim to enhance the understanding of scaling laws by conducting comprehensive evaluations of large recommendation models. Firstly, we investigate the scaling laws across different backbone architectures of the large recommendation models. Secondly, we conduct comprehensive ablation studies to explore the origins of these scaling laws. We then further assess the performance of HSTU, as the representative of large recommendation models, on complex user behavior modeling tasks to evaluate its applicability. Notably, we also analyze its effectiveness in ranking tasks for the first time. Finally, we offer insights into future directions for large recommendation models. Supplementary materials for our research are available on GitHub at https://github.com/USTC-StarTeam/Large-Recommendation-Models.

翻译：推荐系统在各类应用中对于数据筛选与相关信息检索至关重要。近期进展表明，这些系统已整合日益庞大的嵌入表，在工业应用中规模可达数十太字节。然而，传统推荐模型的网络参数扩展已停滞在千万量级，限制了嵌入参数增加带来的进一步效益。受大型语言模型（LLMs）成功的启发，一种采用创新结构扩展网络参数的新方法应运而生，实现了持续的性能提升。该领域的一个重要进展是Meta公司提出的生成式推荐模型HSTU，其通过将参数扩展至数千亿规模，揭示了推荐系统的扩展规律。这一新范式已在在线实验中取得显著性能提升。本文旨在通过对大型推荐模型进行全面评估，深化对扩展规律的理解。首先，我们探究了大型推荐模型在不同骨干架构下的扩展规律。其次，我们进行了系统的消融研究以探索这些扩展规律的起源。随后，我们进一步评估了作为大型推荐模型代表的HSTU在复杂用户行为建模任务上的性能，以评判其适用性。值得注意的是，我们首次分析了其在排序任务中的有效性。最后，我们对大型推荐模型的未来发展方向提出了见解。本研究的补充材料可在GitHub上获取：https://github.com/USTC-StarTeam/Large-Recommendation-Models。