Leveraging long-term user behavioral patterns is a key trajectory for enhancing the accuracy of modern recommender systems. While generative recommender systems have emerged as a transformative paradigm, they face hurdles in effectively modeling extensive historical sequences. To address this challenge, we propose GLASS, a novel framework that integrates long-term user interests into the generative process via SID-Tier and Semantic Search. We first introduce SID-Tier, a module that maps long-term interactions into a unified interest vector to enhance the prediction of the initial SID token. Unlike traditional retrieval models that struggle with massive item spaces, SID-Tier leverages the compact nature of the semantic codebook to incorporate cross features between the user's long-term history and candidate semantic codes. Furthermore, we present semantic hard search, which utilizes generated coarse-grained semantic ID as dynamic keys to extract relevant historical behaviors, which are then fused via an adaptive gated fusion module to recalibrate the trajectory of subsequent fine-grained tokens. To address the inherent data sparsity in semantic hard search, we propose two strategies: semantic neighbor augmentation and codebook resizing. Extensive experiments on two large-scale real-world datasets, TAOBAO-MM and KuaiRec, demonstrate that GLASS outperforms state-of-the-art baselines, achieving significant gains in recommendation quality. Our codes are made publicly available to facilitate further research in generative recommendation.

翻译：利用长期用户行为模式是提升现代推荐系统准确性的关键路径。尽管生成式推荐系统已作为一种变革性范式出现，但在有效建模海量历史序列方面仍面临挑战。为解决这一问题，我们提出GLASS，这是一个通过SID-Tier与语义搜索将长期用户兴趣整合到生成过程中的新型框架。我们首先引入SID-Tier模块，该模块将长期交互映射为统一的兴趣向量以提升初始SID令牌的预测效果。与传统检索模型在海量物品空间中表现受限不同，SID-Tier利用语义码本的紧凑特性，融合用户长期历史与候选语义代码之间的交叉特征。此外，我们提出语义硬搜索方法，该方法以生成的粗粒度语义ID作为动态键来提取相关历史行为，随后通过自适应门控融合模块进行整合，以重新校准后续细粒度令牌的生成轨迹。针对语义硬搜索中固有的数据稀疏性问题，我们提出两种策略：语义邻居增强与码本尺寸调整。在TAOBAO-MM和KuaiRec两个大规模真实数据集上的大量实验表明，GLASS在推荐质量上显著优于现有先进基线模型。我们已公开相关代码以促进生成式推荐领域的进一步研究。