How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation-Reference-Cited by-同舟云学术

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How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation

Published:2024-05-13 Issue: Volume: Page:1726-1732
ISSN:
Container-title:Companion Proceedings of the ACM Web Conference 2024
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Author:

Zhu Lixi¹^ORCID,Huang Xiaowen²^ORCID,Sang Jitao³^ORCID

Affiliation:

1. School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China

2. School of Computer Science and Technology, Beijing Jiaotong University & Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China

3. School of Computer Science and Technology, Beijing Jiaotong University & Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijng, China

Funder

the Fundamental Research Funds for the Central Universities

the National Key Research and Development Program of China under Grant

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589335.3651955

Reference29 articles.

1. Neural Collaborative Filtering

2. Matrix Factorization Techniques for Recommender Systems

3. Advances and challenges in conversational recommender systems: A survey

4. Conversational Recommender System

5. Luke Friedman, Sameer Ahuja, David Allen, Terry Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, et al. Leveraging large language models in conversational recommender systems. arXiv preprint arXiv:2305.07961, 2023.

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