Know Your Users! Estimating User Domain Knowledge in Conversational Recommenders

TL;DR

This paper introduces a new task and dataset for estimating user domain knowledge in conversational recommenders, aiming to improve personalization by adapting to users' expertise levels.

Contribution

The paper presents a novel game-based data collection protocol and dataset to capture user knowledge levels, addressing a gap in existing conversational recommender systems.

Findings

Dataset reveals diverse expressions of user knowledge

Initial analysis shows potential for adaptive CRS improvements

Highlights importance of understanding user expertise in dialogues

Abstract

The ideal conversational recommender system (CRS) acts like a savvy salesperson, adapting its language and suggestions to each user's level of expertise. 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 make a step toward such adaptive systems, we introduce a new task: estimating user domain knowledge from conversations, enabling a CRS to better understand user needs and personalize interactions. A key obstacle to developing such adaptive systems 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 a game-based data collection protocol that elicits varied expressions of knowledge, release the resulting dataset, and provide an initial analysis to highlight its potential for future work on user-knowledge-aware CRS.