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        <title>User Simulator Assisted Open-ended Conversational Recommendation System</title>
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    <abstract>Conversational recommendation systems (CRS) have gained popularity in e-commerce as they can recommend items during user interactions. However, current open-ended CRS have limited recommendation performance due to their short-sighted training process, which only predicts one utterance at a time without considering its future impact. To address this, we propose a User Simulator (US) that communicates with the CRS using natural language based on given user preferences, enabling long-term reinforcement learning. We also introduce a framework that uses reinforcement learning (RL) with two novel rewards, i.e., recommendation and conversation rewards, to train the CRS. This approach considers the long-term goals and improves both the conversation and recommendation performance of the CRS. Our experiments show that our proposed framework improves the recall of recommendations by almost 100%. Moreover, human evaluation demonstrates the superiority of our framework in enhancing the informativeness of generated utterances.</abstract>
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%0 Conference Proceedings
%T User Simulator Assisted Open-ended Conversational Recommendation System
%A Zhan, Qiusi
%A Guo, Xiaojie
%A Ji, Heng
%A Wu, Lingfei
%Y Chen, Yun-Nung
%Y Rastogi, Abhinav
%S Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhan-etal-2023-user
%X Conversational recommendation systems (CRS) have gained popularity in e-commerce as they can recommend items during user interactions. However, current open-ended CRS have limited recommendation performance due to their short-sighted training process, which only predicts one utterance at a time without considering its future impact. To address this, we propose a User Simulator (US) that communicates with the CRS using natural language based on given user preferences, enabling long-term reinforcement learning. We also introduce a framework that uses reinforcement learning (RL) with two novel rewards, i.e., recommendation and conversation rewards, to train the CRS. This approach considers the long-term goals and improves both the conversation and recommendation performance of the CRS. Our experiments show that our proposed framework improves the recall of recommendations by almost 100%. Moreover, human evaluation demonstrates the superiority of our framework in enhancing the informativeness of generated utterances.
%R 10.18653/v1/2023.nlp4convai-1.8
%U https://aclanthology.org/2023.nlp4convai-1.8
%U https://doi.org/10.18653/v1/2023.nlp4convai-1.8
%P 89-101

Markdown (Informal)

[User Simulator Assisted Open-ended Conversational Recommendation System](https://aclanthology.org/2023.nlp4convai-1.8) (Zhan et al., NLP4ConvAI 2023)

• User Simulator Assisted Open-ended Conversational Recommendation System (Zhan et al., NLP4ConvAI 2023)

ACL

• Qiusi Zhan, Xiaojie Guo, Heng Ji, and Lingfei Wu. 2023. User Simulator Assisted Open-ended Conversational Recommendation System. In Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023), pages 89–101, Toronto, Canada. Association for Computational Linguistics.