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arXiv · 2301.05544

UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems

Jafar Afzali·Aleksander Mark Drzewiecki·Krisztian Balog·Shuo Zhang
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Abstract

We present an extensible user simulation toolkit to facilitate automatic evaluation of conversational recommender systems. It builds on an established agenda-based approach and extends it with several novel elements, including user satisfaction prediction, persona and context modeling, and conditional natural language generation. We showcase the toolkit with a pre-existing movie recommender system and demonstrate its ability to simulate dialogues that mimic real conversations, while requiring only a handful of manually annotated dialogues as training data.

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Jafar Afzali, Aleksander Mark Drzewiecki, Krisztian Balog, Shuo Zhang. 2023-01-13. UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems. https://doi.org/10.1145/3539597.3573029

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