The ideal conversational recommender system (CRS) acts like a savvy salesperson, adapting its language and suggestions to a user's expertise level. However, most current systems treat all users as experts, leading to frustrating and inefficient interactions when users are unfamiliar with a domain. Systems that can adapt their conversational strategies to a user's knowledge level stand to offer a much more natural and effective experience. To enable such adaptation, a CRS must first be able to estimate a user's domain knowledge from interaction signals. Yet, accurately estimating knowledge typically requires tailored interactions to elicit those signals in the first place, creating a fundamental chicken-and-egg problem. In this work, we take a first step toward breaking this dependency by introducing a new task: estimating user domain knowledge directly from conversational transcripts. A key obstacle to such estimation is the lack of suitable data; to our knowledge, no existing dataset captures the conversational behaviors of users with varying levels of domain knowledge. Furthermore, in most dialogue collection protocols, users are free to express their own preferences, which tends to concentrate on popular items and well-known features, offering little insight into how novices explore or learn about unfamiliar features. To address this, we design RecQuest, a game-with-a-purpose data collection protocol that elicits varied expressions of knowledge while using a target-aware CRS to guide interactions, release the resulting dataset, and provide baseline methods and analyses to support future work on user-knowledge-aware CRS.

翻译：摘要：理想的对话式推荐系统如同一位精明的销售员，能根据用户的专业知识水平调整语言表达和推荐内容。然而，当前大多数系统将所有用户视为专家，当用户不熟悉某领域时，会导致令人沮丧且低效的交互。能够根据用户知识水平调整对话策略的系统，有望提供更自然、更有效的体验。为实现这种自适应性，对话式推荐系统首先需要能够从交互信号中估计用户的领域知识。然而，准确估计知识水平通常需要量身定制的交互来先发获取这些信号，这形成了一个根本性的“先有鸡还是先有蛋”问题。在这项工作中，我们通过引入一项新任务来迈出打破这种依赖关系的第一步：直接从对话记录中估计用户领域知识。实现这种估计的一个关键障碍是缺乏合适的数据——据我们所知，现有数据集均未捕捉到具有不同领域知识水平用户的对话行为。此外，在大多数对话收集协议中，用户可自由表达偏好，这往往导致对话集中在热门物品和广为人知的特征上，难以揭示新手如何探索或了解陌生特征。为解决此问题，我们设计了RecQuest——一种有目的的游戏式数据收集协议，该协议能在利用目标感知型对话式推荐系统引导交互的同时，激发用户对知识的不同表达方式；我们发布了由此产生的数据集，并提供了基准方法和分析，以支持未来面向用户知识感知的对话式推荐系统的研究。