While generative recommendations (GR) possess strong sequential reasoning capabilities, they face significant challenges when processing extremely long user behavior sequences: the high computational cost forces practical sequence lengths to be limited, preventing models from capturing users' lifelong interests; meanwhile, the inherent "recency bias" of attention mechanisms further weakens learning from long-term history. To overcome this bottleneck, we propose GEMs (Generative rEcommendation with a Multi-stream decoder), a novel and unified framework designed to break the long-sequence barrier by capturing users' lifelong interaction sequences through a multi-stream perspective. Specifically, GEMs partitions user behaviors into three temporal streams$\unicode{x2014}$Recent, Mid-term, and Lifecycle$\unicode{x2014}$and employs tailored inference schemes for each: a one-stage real-time extractor for immediate dynamics, a lightweight indexer for cross attention to balance accuracy and cost for mid-term sequences, and a two-stage offline-online compression module for lifelong modeling. These streams are integrated via a parameter-free fusion strategy to enable holistic interest representation. Extensive experiments on large-scale industrial datasets demonstrate that GEMs significantly outperforms state-of-the-art methods in recommendation accuracy. Notably, GEMs is the first lifelong GR framework successfully deployed in a high-concurrency industrial environment, achieving superior inference efficiency while processing user sequences of over 100,000 interactions.

翻译：尽管生成式推荐模型具备强大的序列推理能力，但在处理极长用户行为序列时面临显著挑战：高昂的计算成本迫使实际序列长度受限，导致模型无法捕捉用户的终身兴趣；同时，注意力机制固有的"近因偏好"进一步削弱了对长期历史的学习。为突破这一瓶颈，我们提出GEMs（基于多流解码器的生成式推荐框架），这是一个新颖统一的框架，旨在通过多流视角捕捉用户终身交互序列，从而打破长序列壁垒。具体而言，GEMs将用户行为划分为三个时序流——近期流、中期流与生命周期流，并为每个流设计定制化推理方案：针对即时动态采用单阶段实时提取器，对中期序列采用轻量级索引器进行交叉注意力以平衡精度与成本，而对终身建模则采用两阶段离线-在线压缩模块。这些流通过无参数融合策略进行整合，实现整体兴趣表征。基于大规模工业数据集的实验表明，GEMs在推荐准确率上显著优于现有最优方法。值得注意的是，GEMs是首个成功部署于高并发工业环境的终身生成式推荐框架，在处理超过10万次交互的用户序列时仍能保持卓越的推理效率。