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The rise of Large Language Models (LLMs) has sparked interest in their application to sequential recommendation tasks as they can provide supportive item information.
Self-attentive sequential recommendation
W. Kang and J. J. McAuley · 2018
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang · 2019
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Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
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M6-rec: Generative pretrained language models are open-ended recommender systems
Z. Cui, J. Ma, C. Zhou, J. Zhou, and H. Yang · 2022
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Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
S. Geng, S. Liu, Z. Fu, Y. Ge, and Y. Zhang · 2022
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Rethinking with retrieval: Faithful large language model inference
H. He, H. Zhang, and D. Roth · 2022
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Towards universal sequence representation learning for recommender systems
Y. Hou, S. Mu, W. X. Zhao, Y. Li, B. Ding, and J.-R. Wen · 2022
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Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
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Contrastive learning for representation degeneration problem in sequential recommendation
R. Qiu, Z. Huang, H. Yin, and Z. Wang · 2022
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
H. Trivedi, N. Balasubramanian, T. Khot, and A. Sabharwal · 2022
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Chain-of-thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al · 2022
Cited alongside, same era.
Large language models are reasoners with self-verification
Y. Weng, M. Zhu, S. He, K. Liu, and J. Zhao · 2022
Cited alongside, same era.
Automatic chain of thought prompting in large language models
Z. Zhang, A. Zhang, M. Li, and A. Smola · 2022
Cited alongside, same era.
Palr: Personalization aware llms for recommendation
Z. Chen · 2023
Cited alongside, same era.
Chat-rec: Towards interactive and explainable llms-augmented recommender system
Y. Gao, T. Sheng, Y. Xiang, Y. Xiong, H. Wang, and J. Zhang · 2023
Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, et al · 2023
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Knowledge injection to counter large language model (llm) hallucination
A. Martino, M. Iannelli, and C. Truong · 2023
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Large language models are competitive near cold-start recommenders for language-and item-based preferences
S. Sanner, K. Balog, F. Radlinski, B. Wedin, and L. Dixon · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
N. Shinn, B. Labash, and A. Gopinath · 2023
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Can large language models really improve by self-critiquing their own plans?
K. Valmeekam, M. Marquez, and S. Kambhampati · 2023
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Cited alongside, same era.
Leveraging large language models for sequential recommendation
J. Harte, W. Zorgdrager, P. Louridas, A. Katsifodimos, D. Jannach, and M. Fragkoulis · 2023
Cited alongside, same era.
Learning vector-quantized item representation for transferable sequential recommenders
Y. Hou, Z. He, J. McAuley, and W. X. Zhao · 2023
Cited alongside, same era.
Large language models cannot self-correct reasoning yet
J. Huang, X. Chen, S. Mishra, H. S. Zheng, A. W. Yu, X. Song, and D. Zhou · 2023
Cited alongside, same era.
J. Lin, R. Shan, C. Zhu, K. Du, B. Chen, S. Quan, R. Tang, Y. Yu, and W. Zhang · 2023
Cited alongside, same era.
Deductive verification of chain-of-thought reasoning
Z. Ling, Y. Fang, X. Li, Z. Huang, M. Lee, R. Memisevic, and H. Su · 2023
Cited alongside, same era.
A bi-step grounding paradigm for large language models in recommendation systems
K. Bao, J. Zhang, W. Wang, Y. Zhang, Z. Yang, Y. Luo, F. Feng, X. He, and Q. Tian
Cited in the paper.
K. Bao, J. Zhang, Y. Zhang, W. Wang, F. Feng, and X. He
Cited in the paper.
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Zero-shot next-item recommendation using large pretrained language models
L. Wang and E.-P. Lim · 2023
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Exploring large language model for graph data understanding in online job recommendations
L. Wu, Z. Qiu, Z. Zheng, H. Zhu, and E. Chen · 2023
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Llm lies: Hallucinations are not bugs, but features as adversarial examples
J.-Y. Yao, K.-P. Ning, Z.-H. Liu, M.-N. Ning, and L. Yuan · 2023
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Siren’s song in the ai ocean: A survey on hallucination in large language models
Y. Zhang, Y. Li, L. Cui, D. Cai, L. Liu, T. Fu, X. Huang, E. Zhao, Y. Zhang, Y. Chen, et al · 2023
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Contrastvae: Contrastive variational autoencoder for sequential recommendation
Y. Wang, H. Zhang, Z. Liu, L. Yang, and P. S. Yu · 2066
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