This paper introduces BLaIR, a series of pretrained sentence embedding models specialized for recommendation scenarios. BLaIR is trained to learn correlations between item metadata and potential natural language context, which is useful for retrieving and recommending items. To pretrain BLaIR, we collect Amazon Reviews 2023, a new dataset comprising over 570 million reviews and 48 million items from 33 categories, significantly expanding beyond the scope of previous versions. We evaluate the generalization ability of BLaIR across multiple domains and tasks, including a new task named complex product search, referring to retrieving relevant items given long, complex natural language contexts. Leveraging large language models like ChatGPT, we correspondingly construct a semi-synthetic evaluation set, Amazon-C4. Empirical results on the new task, as well as conventional retrieval and recommendation tasks, demonstrate that BLaIR exhibit strong text and item representation capacity. Our datasets, code, and checkpoints are available at: https://github.com/hyp1231/AmazonReviews2023.

翻译：本文提出BLaIR系列预训练句子嵌入模型，专为推荐场景设计。BLaIR通过训练学习物品元数据与潜在自然语言上下文之间的关联，从而有效支持物品检索与推荐任务。为完成BLaIR的预训练，我们构建了Amazon Reviews 2023数据集，包含来自33个类别的逾5.7亿条评论与4800万件物品，数据规模较先前版本显著扩展。我们评估了BLaIR在多个领域与任务中的泛化能力，其中包含一项名为“复杂产品搜索”的新任务——即根据冗长、复杂的自然语言上下文检索相关物品。借助ChatGPT等大型语言模型，我们同步构建了半合成评估集Amazon-C4。在新任务及传统检索与推荐任务上的实证结果表明，BLaIR具备强大的文本与物品表征能力。数据集、代码与模型检查点已开源于：https://github.com/hyp1231/AmazonReviews2023。