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，要求模型具备高效性与有效性。近年来，生成式检索已成为文档检索领域新兴范式，展现出显著性能。此类方法具有端到端可微分的优势，表明其在推荐系统中的可行性。然而，这些方法在大规模推荐场景中效率与效果均有不足。为兼顾效率与效果，本文提出生成式检索框架SEATER，通过对比学习学习语义树结构物品标识符。具体而言，我们采用编码器-解码器模型从历史行为中提取用户兴趣，并通过树结构物品标识符检索候选物品。SEATER设计了平衡的k叉树结构物品标识符，为每个令牌独立分配语义空间。该策略确保同层语义一致性，而不同层级对应不同语义粒度。该结构同时为所有物品保持稳定且快速的推理速度。针对树结构特性，SEATER学习标识符令牌的语义、层级关系及跨令牌依赖。为此，我们结合生成任务引入两项对比学习任务，协同优化模型与标识符。InfoNCE损失根据层级位置对齐令牌嵌入，三元组损失按期望顺序对相似标识符进行排序。通过上述设计，SEATER同时实现了高效性与有效性。在三个公开数据集及一个工业数据集上的大量实验表明，SEATER显著优于当前最优模型。