Fetching the paper…
Reading the bibliography…
Owing to powerful natural language processing and generative capabilities, large language model (LLM) agents have emerged as a promising solution for enhancing recommendation systems via user simulation.
R. S. Sutton, A. G. Barto
1998
Earlier work this paper cites.
P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck, “Learning deep structured semantic models for web search using clickthrough data,” in
2013
Earlier work this paper cites.
Y. Huang, B. Cui, W. Zhang, J. Jiang, and Y. Xu, “Tencentrec: Real-time stream recommendation in practice,” in
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,”
2015
Earlier work this paper cites.
R. He, C. Fang, Z. Wang, and J. McAuley, “Vista: A visually, socially, and temporally-aware model for artistic recommendation,” in
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
L. Zheng, C.-T. Lu, F. Jiang, J. Zhang, and P. S. Yu, “Spectral collaborative filtering,” in
2018
Earlier work this paper cites.
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in
2018
Earlier work this paper cites.
W.-C. Kang and J. McAuley, “Self-attentive sequential recommendation,” in
2018
Earlier work this paper cites.
F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang, “Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,” in
2019
Earlier work this paper cites.
F. Yuan, A. Karatzoglou, I. Arapakis, J. M. Jose, and X. He, “A simple convolutional generative network for next item recommendation,” in
2019
Earlier work this paper cites.
J.-C. Shi, Y. Yu, Q. Da, S.-Y. Chen, and A.-X. Zeng, “Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning,” in
2019
Earlier work this paper cites.
X. Chen, S. Li, H. Li, S. Jiang, Y. Qi, and L. Song, “Generative adversarial user model for reinforcement learning based recommendation system,” in
2019
Earlier work this paper cites.
A. Agarwal, N. Jiang, S. M. Kakade, and W. Sun, “Reinforcement learning: Theory and algorithms,”
2019
Earlier work this paper cites.
J. Yang, X. Yi, D. Zhiyuan Cheng, L. Hong, Y. Li, S. Xiaoming Wang, T. Xu, and E. H. Chi, “Mixed negative sampling for learning two-tower neural networks in recommendations,” in
2020
Earlier work this paper cites.
F. Yuan, X. He, A. Karatzoglou, and L. Zhang, “Parameter-efficient transfer from sequential behaviors for user modeling and recommendation,” in
2020
Earlier work this paper cites.
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark
2021
Cited alongside, same era.
K. Bao, J. Zhang, Y. Zhang, W. Wang, F. Feng, and X. He, “Tallrec: An effective and efficient tuning framework to align large language model with recommendation,” in
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Shang, C. Gao, J. Chen, D. Jin, H. Ma, and Y. Li, “Enhancing adversarial robustness of multi-modal recommendation via modality balancing,” in
2023
Cited alongside, same era.
Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, X. Bi, H. Zhang, M. Zhang, Y. Li, Y. Wu
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Hou, J. Zhang, Z. Lin, H. Lu, R. Xie, J. McAuley, and W. X. Zhao, “Large language models are zero-shot rankers for recommender systems,” in
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
K. Zhao, S. Liu, Q. Cai, X. Zhao, Z. Liu, D. Zheng, P. Jiang, and K. Gai, “Kuaisim: A comprehensive simulator for recommender systems,”
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
X. Luo, J. Cao, T. Sun, J. Yu, R. Huang, W. Yuan, H. Lin, Y. Zheng, S. Wang, Q. Hu
2024
Cited alongside, same era.
2024
Later among the works it cites.
A. Yang, B. Zhang, B. Hui, B. Gao, B. Yu, C. Li, D. Liu, J. Tu, J. Zhou, J. Lin
2024
Later among the works it cites.
H. Ying, S. Zhang, L. Li, Z. Zhou, Y. Shao, Z. Fei, Y. Ma, J. Hong, K. Liu, Z. Wang
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Jia, Y. Wang, Y. Li, H. Chen, X. Bai, Z. Liu, J. Liang, Q. Chen, H. Li, P. Jiang
2025
Closest in time.
Z. Zhang, S. Liu, Z. Liu, R. Zhong, Q. Cai, X. Zhao, C. Zhang, Q. Liu, and P. Jiang, “Llm-powered user simulator for recommender system,” in
2025
Closest in time.
Y. Ye, Z. Zheng, Y. Shen, T. Wang, H. Zhang, P. Zhu, R. Yu, K. Zhang, and H. Xiong, “Harnessing multimodal large language models for multimodal sequential recommendation,” in
2025
Closest in time.
L. Wang, J. Zhang, H. Yang, Z.-Y. Chen, J. Tang, Z. Zhang, X. Chen, Y. Lin, H. Sun, R. Song
2025
Closest in time.
2025
Closest in time.
D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi
2025
Closest in time.
2025
Closest in time.