Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. To the best of our knowledge, this is the first Semantic ID-based generative model for recommendation tasks. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.

翻译：现代推荐系统通过首先将查询和候选项目嵌入到同一统一空间，随后借助近似最近邻搜索从查询嵌入中选出最优候选项目，从而执行大规模检索。本文提出一种新颖的生成式检索方法，其中检索模型以自回归方式解码目标候选项目的标识符。为此，我们构建了具有语义意义的码字元组，作为每个项目的语义ID。给定用户会话中项目的语义ID后，我们训练一个基于Transformer的序列到序列模型，以预测用户接下来将交互的项目的语义ID。据我们所知，这是首个基于语义ID的生成式模型用于推荐任务。实验表明，采用所提范式训练的推荐系统在多个数据集上显著优于当前最先进模型。此外，我们证明将语义ID融入序列到序列模型能增强其泛化能力，具体表现为对无历史交互项目的检索性能提升。