构建3.95亿条真实搜索日志的自动补全数据集，推动搜索推荐系统研究。
AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset
- 基于亚马逊真实搜索日志构建大规模自然语言补全数据集
- 微调大模型在上下文感知下表现最优，但仅达理论上限一半
- 适合研究搜索推荐、上下文建模与生成式AI的开发者使用
查询自动补全（QAC）是现代搜索引擎的关键功能，通过输入前缀预测用户搜索意图。尽管广泛应用，缺乏大规模真实数据集限制了系统发展。本文提出AmazonQAC，来自亚马逊搜索日志的3.95亿条样本数据集，包含实际用户输入前缀到完整搜索词的序列，以及会话ID和时间戳，支持上下文建模。我们评估了前缀树、语义检索和大语言模型（含/不含微调）的表现。结果表明，微调后的语言模型结合上下文信息效果最佳。然而，即使最优系统在测试数据上也仅达到理论可能性能的一半，说明当前QAC仍具挑战性且未被充分解决。本工作旨在推动该领域研究，以更好服务多样化场景。数据已开源至Hugging Face：https://huggingface.co/datasets/amazon/AmazonQAC。
原文摘要 · Abstract (English)
Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread adoption, the absence of large-scale, realistic datasets has hindered advancements in QAC system development. This paper addresses this gap by introducing AmazonQAC, a new QAC dataset sourced from Amazon Search logs, comprising 395M samples. The dataset includes actual sequences of user-typed prefixes leading to final search terms, as well as session IDs and timestamps that support modeling the context-dependent aspects of QAC. We assess Prefix Trees, semantic retrieval, and Large Language Models (LLMs) with and without finetuning. We find that finetuned LLMs perform best, particularly when incorporating contextual information. However, even our best system achieves only half of what we calculate is theoretically possible on our test data, which implies QAC is a challenging problem that is far from solved with existing systems. This contribution aims to stimulate further research on QAC systems to better serve user needs in diverse environments. We open-source this data on Hugging Face at https://huggingface.co/datasets/amazon/AmazonQAC.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。