研究人员开发了CaLIR，一个用于电子商务生成式检索的新框架，旨在提高搜索的准确性和效率。CaLIR使用类别层次结构来指导潜在意图推理，解决了将简短、嘈杂的用户查询映射到产品标识符的挑战。这种方法在检索有效性和低延迟要求之间取得平衡，并在不同数据集和生成模型上展示了鲁棒性。 AI
影响 为电子商务搜索引入了一种新颖的生成式检索方法，平衡了准确性和效率。
排序理由 该集群包含一篇详细介绍生成式检索新框架的研究论文。
在 arXiv cs.IR (Information Retrieval) 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
Industrial e-commerce search serves hundreds of millions of items through a multi-branch retrieval stage fused by hand-tuned merging without joint optimization. Generative retrieval (GR) raises the prospect of collapsing this stage into a single model, yet unification is gated by…
Generative retrieval offers a new paradigm for e-commerce search by mapping user queries directly to product Semantic Identifiers (SIDs). However, e-commerce queries are often short, noisy, attribute-heavy, and associated with multiple category-consistent products, creating a sub…
Generative retrieval offers a new paradigm for e-commerce search by mapping user queries directly to product Semantic Identifiers (SIDs). However, e-commerce queries are often short, noisy, attribute-heavy, and associated with multiple category-consistent products, creating a sub…