用决策树生成购物对话，让智能助手更精准找到用户想要的商品。
Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching
- 基于决策树规划对话路径，确保在最少步骤内找到目标商品。
- 构建了3600条自然连贯的购物对话数据集，覆盖三个领域。
- 适合研究对话系统、电商智能助手的开发者和研究人员。
对话式商品搜索（CPS）的目标是开发一个能与顾客直接交互的智能购物助手，理解购物意图、提出澄清问题并找到相关商品。然而，训练此类助手主要受限于缺乏可靠且大规模的数据集。以往的人工标注CPS数据集规模极小，且未与真实商品搜索系统集成。本文提出一种新方法TRACER，利用大语言模型（LLMs）生成不同购物领域的逼真自然对话。TRACER的独特之处在于将生成过程锚定于对话计划——由决策树模型预测的产品搜索轨迹，确保以最少的搜索条件发现相关商品。我们还发布了首个面向目标的CPS数据集Wizard of Shopping（WoS），包含来自三个购物领域的3600条高度自然且连贯的对话。最后，通过人工评估和下游任务验证了WoS的质量与有效性。
原文摘要 · Abstract (English)
The goal of conversational product search (CPS) is to develop an intelligent, chat-based shopping assistant that can directly interact with customers to understand shopping intents, ask clarification questions, and find relevant products. However, training such assistants is hindered mainly due to the lack of reliable and large-scale datasets. Prior human-annotated CPS datasets are extremely small in size and lack integration with real-world product search systems. We propose a novel approach, TRACER, which leverages large language models (LLMs) to generate realistic and natural conversations for different shopping domains. TRACER's novelty lies in grounding the generation to dialogue plans, which are product search trajectories predicted from a decision tree model, that guarantees relevant product discovery in the shortest number of search conditions. We also release the first target-oriented CPS dataset Wizard of Shopping (WoS), containing highly natural and coherent conversations (3.6k) from three shopping domains. Finally, we demonstrate the quality and effectiveness of WoS via human evaluations and downstream tasks.
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