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.- Anthology ID:
- 2023.nlp4convai-1.8
- Volume:
- Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Yun-Nung Chen, Abhinav Rastogi
- Venue:
- NLP4ConvAI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–101
- Language:
- URL:
- https://aclanthology.org/2023.nlp4convai-1.8
- DOI:
- 10.18653/v1/2023.nlp4convai-1.8
- Cite (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.
- Cite (Informal):
- User Simulator Assisted Open-ended Conversational Recommendation System (Zhan et al., NLP4ConvAI 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.nlp4convai-1.8.pdf