Dynamic Open-book Prompt for Conversational Recommender System

Xuan Ma, Tieyun Qian, Ke Sun


Abstract
Conversational Recommender System (CRS) aims to deliver personalized recommendations through interactive dialogues. Recent advances in prompt learning have shed light on this task. However, the performance of existing methods is confined by the limited context within ongoing conversations. Moreover, these methods utilize training samples only for prompt parameter training. The constructed prompt lacks the ability to refer to the training data during inference, which exacerbates the problem of limited context. To solve this problem, we propose a novel Dynamic Open-book Prompt approach, where the open book stores users’ experiences in historical data, and we dynamically construct the prompt to memorize the user’s current utterance and selectively retrieve relevant contexts from the open book. Specifically, we first build an item-recommendation graph from the open book and convolute on the graph to form a base prompt which contains more information besides the finite dialogue. Then, we enhance the representation learning process of the prompt by tailoring similar contexts in the graph into the prompt to meet the user’s current need. This ensures the prompt provides targeted suggestions that are both informed and contextually relevant. Extensive experimental results on the ReDial dataset demonstrate the significant improvements achieved by our proposed model over the state-of-the-art methods. Our code and data are available at https://github.com/NLPWM-WHU/DOP.
Anthology ID:
2023.findings-emnlp.658
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9839–9849
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.658
DOI:
10.18653/v1/2023.findings-emnlp.658
Bibkey:
Cite (ACL):
Xuan Ma, Tieyun Qian, and Ke Sun. 2023. Dynamic Open-book Prompt for Conversational Recommender System. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9839–9849, Singapore. Association for Computational Linguistics.
Cite (Informal):
Dynamic Open-book Prompt for Conversational Recommender System (Ma et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-emnlp.658.pdf