我们考虑产品搜索中的语义匹配问题：给定一个客户查询，从一个大小为1亿或更多的巨大目录中检索所有语义相关的产品。由于大的目录空间和实时延迟的限制，语义匹配算法不仅需要高召回率，而且还需要具有低延迟。传统的词汇匹配方法（例如，Okapi-BM 25）利用倒排索引来实现快速推理时间，但未能捕获查询和产品之间的行为信号。相比之下，基于嵌入的模型从客户行为数据中学习语义表示，但由于延迟限制，性能通常受到浅层神经编码器的限制。语义产品搜索可以被看作是一个极限多标签分类（XMC）问题，其中客户查询是输入实例，产品是输出标签。在本文中，我们的目标是提高语义产品搜索使用基于树的XMC模型，推理时间复杂度是对数的产品数量。我们考虑层次线性模型与n-gram功能的快速实时推理。定量地，我们的方法保持了每个查询1.25毫秒的低延迟，并实现了Recall@100的65%的改进（60.9% vs. 36.8%），而不是基于嵌入的竞争DSSM模型。我们的模型是强大的权重修剪不同的阈值，它可以灵活地满足不同的系统要求的在线部署。质量，我们的方法可以检索产品，补充现有的产品搜索系统，并增加多样性的匹配集。
英文摘要：
We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and real-time latency constraints, semantic matching algorithms not only desire high recall but also need to have low latency. Conventional lexical matching approaches (e.g., Okapi-BM25) exploit inverted indices to achieve fast inference time, but fail to capture behavioral signals between queries and products. In contrast, embedding-based models learn semantic representations from customer behavior data, but the performance is often limited by shallow neural encoders due to latency constraints. Semantic product search can be viewed as an eXtreme Multi-label Classification (XMC) problem, where customer queries are input instances and products are output labels. In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products. We consider hierarchical linear models with n-gram features for fast real-time inference. Quantitatively, our method maintains a low latency of 1.25 milliseconds per query and achieves a 65% improvement of Recall@100 (60.9% v.s. 36.8%) over a competing embedding-based DSSM model. Our model is robust to weight pruning with varying thresholds, which can flexibly meet different system requirements for online deployments. Qualitatively, our method can retrieve products that are complementary to existing product search system and add diversity to the match set.
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗肯科特中心）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
