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This paper investigates how large language models (LLMs) can enhance recommender systems, with a specific focus on Conversational Recommender Systems that leverage user preferences and personalised candidate selections from existing ranking models.
Language models are few-shot learners,
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Y. Hu, Y. Koren, C. Volinsky, · 2008
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Matrix factorization techniques for recommender systems,
Y. Koren, R. Bell, C. Volinsky, · 2009
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Attention is all you need,
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, · 2017
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Sequential user-based recurrent neural network recommendations,
T. Donkers, B. Loepp, J. Ziegler, · 2017
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Improving language understanding by generative pre-training,
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, · 2018
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Self-attentive sequential recommendation,
W.-C. Kang, J. McAuley, · 2018
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Deep learning recommendation model for personalization and recommendation systems,
M. Naumov, D. Mudigere, H.-J. M. Shi, J. Huang, N. Sundaraman, J. Park, X. Wang, U. Gupta, C.-J. Wu, A. G. Azzolini, et al., · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding,
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, · 2019
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Language models are unsupervised multitask learners,
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, · 2019
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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, P. Jiang, · 2019
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Behavior sequence transformer for e-commerce recommendation in alibaba,
Q. Chen, H. Zhao, W. Li, P. Huang, W. Ou, · 2019
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The effect of social presence and chatbot errors on trust,
D. C. Toader, G. D. Boca, R. Toader, M. Macelaru, C. Toader, D. S. Ighian, A. T. G. Rădulescu, · 2019
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Recommendations at videoland,
M. Gutierrez Granada, D. Odijk, · 2021
2023
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Gpt4rec: A generative framework for personalized recommendation and user interests interpretation,
J. Li, W. Zhang, T. Wang, G. Xiong, A. Lu, G. G. Medioni, · 2023
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Is chatgpt a good recommender? a preliminary study,
J. Liu, C. Liu, R. Lv, K. Zhou, Y. B. Zhang, · 2023
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Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,
P. P. Ray, · 2023
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Unifying large language models and knowledge graphs: A roadmap,
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Palm: Scaling language modeling with pathways,
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al., · 2022
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M6-rec: Generative pretrained language models are open-ended recommender systems,
Z. Cui, J. Ma, C. Zhou, J. Zhou, H. Yang, · 2022
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S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, X. Wu, · 2023
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Scoping fairness objectives and identifying fairness metrics for recommender systems: The practitioners’ perspective,
J. J. Smith, L. Beattie, H. Cramer, · 2023
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“i think you might like this”: Exploring effects of confidence signal patterns on trust in and reliance on conversational recommender systems,
M. Radensky, J. A. Séguin, J. S. Lim, K. Olson, R. Geiger, · 2023
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