Modern large-scale recommendation systems rely heavily on user interaction history sequences to enhance the model performance. The advent of large language models and sequential modeling techniques, particularly transformer-like architectures, has led to significant advancements recently (e.g., HSTU, SIM, and TWIN models). While scaling to ultra-long user histories (10k to 100k items) generally improves model performance, it also creates significant challenges on latency, queries per second (QPS) and GPU cost in industry-scale recommendation systems. Existing models do not adequately address these industrial scalability issues. In this paper, we propose a novel two-stage modeling framework, namely VIrtual Sequential Target Attention (VISTA), which decomposes traditional target attention from a candidate item to user history items into two distinct stages: (1) user history summarization into a few hundred tokens; followed by (2) candidate item attention to those tokens. These summarization token embeddings are then cached in storage system and then utilized as sequence features for downstream model training and inference. This novel design for scalability enables VISTA to scale to lifelong user histories (up to one million items) while keeping downstream training and inference costs fixed, which is essential in industry. Our approach achieves significant improvements in offline and online metrics and has been successfully deployed on an industry leading recommendation platform serving billions of users.

翻译：现代大规模推荐系统高度依赖用户交互历史序列来提升模型性能。大语言模型与序列建模技术（特别是Transformer类架构）的涌现推动了该领域的重大进展（例如HSTU、SIM和TWIN模型）。虽然将用户历史扩展至超长序列（1万至10万条物品）通常能提升模型性能，但在工业级推荐系统中会引发延迟、每秒查询数和GPU成本等方面的重大挑战。现有模型未能充分解决这些工业化扩展性问题。本文提出一种新颖的两阶段建模框架——虚拟序列目标注意力（VISTA），该框架将传统的候选物品对用户历史物品的目标注意力分解为两个独立阶段：（1）将用户历史摘要压缩为数百个令牌；（2）候选物品对这些摘要令牌的注意力。这些摘要令牌嵌入可缓存至存储系统，并作为序列特征用于下游模型训练与推理。这种创新性的可扩展设计使VISTA能够处理终身用户历史（最高百万级物品），同时保持下游训练与推理成本固定——这在工业界至关重要。本方法在离线与在线指标上均取得显著提升，并已成功部署于服务数十亿用户的行业领先推荐平台。