The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradigm for document retrieval, showing notable performance. These methods enjoy merits like being end-to-end differentiable, suggesting their viability in recommendation. However, these methods fall short in efficiency and effectiveness for large-scale recommendations. To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning. Specifically, we employ an encoder-decoder model to extract user interests from historical behaviors and retrieve candidates via tree-structured item identifiers. SEATER devises a balanced k-ary tree structure of item identifiers, allocating semantic space to each token individually. This strategy maintains semantic consistency within the same level, while distinct levels correlate to varying semantic granularities. This structure also maintains consistent and fast inference speed for all items. Considering the tree structure, SEATER learns identifier tokens' semantics, hierarchical relationships, and inter-token dependencies. To achieve this, we incorporate two contrastive learning tasks with the generation task to optimize both the model and identifiers. The infoNCE loss aligns the token embeddings based on their hierarchical positions. The triplet loss ranks similar identifiers in desired orders. In this way, SEATER achieves both efficiency and effectiveness. Extensive experiments on three public datasets and an industrial dataset have demonstrated that SEATER outperforms state-of-the-art models significantly.

翻译：检索阶段是推荐系统中的关键组成部分，要求模型兼具高效性与有效性。近年来，生成式检索已成为文档检索的新兴范式，展现出显著性能。这类方法具有端到端可微分等优点，表明其在推荐场景中具有可行性。然而，现有方法在大规模推荐任务中仍面临效率与性能的不足。为实现效率与性能的平衡，本文提出一种生成式检索框架SEATER，该框架通过对比学习构建语义树结构项目标识符。具体而言，我们采用编码器-解码器模型从用户历史行为中提取兴趣偏好，并通过树结构项目标识符检索候选项目。SEATER设计了平衡k叉树结构的项目标识符，为每个标识符单元独立分配语义空间。该策略保持了同一层级内的语义一致性，而不同层级对应差异化的语义粒度。此结构同时确保了所有项目具有一致且快速的推理速度。针对树形结构特性，SEATER同步学习标识符单元的语义信息、层级关系及单元间依赖关系。为此，我们在生成任务中融入了两项对比学习任务以联合优化模型与标识符：基于infoNCE损失的对比任务根据层级位置对齐单元嵌入表示；基于三元组损失的对比任务使相似标识符按预期顺序排列。通过这种方式，SEATER实现了效率与性能的双重提升。在三个公共数据集及一个工业数据集上的大量实验表明，SEATER显著优于现有最优模型。