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With the significant successes of large language models (LLMs) in many natural language processing tasks, there is growing interest among researchers in exploring LLMs for novel recommender systems.
The original Borda count and partial voting
Emerson, P. 2013 · 2013
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Deep CTR Prediction in Display Advertising
Chen, J.; Sun, B.; Li, H.; Lu, H.; and Hua, X.-S. 2016 · 2016
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The MovieLens Datasets: History and Context
Harper, F. M.; and Konstan, J. A. 2016 · 2016
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
He, R.; and McAuley, J. J. 2016 · 2016
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Session-based Recommendations with Recurrent Neural Networks
Hidasi, B.; Karatzoglou, A.; Baltrunas, L.; and Tikk, D. 2016 · 2016
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Neural Collaborative Filtering
He, X.; Liao, L.; Zhang, H.; Nie, L.; Hu, X.; and Chua, T. 2017 · 2017
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Self-Attentive Sequential Recommendation
Kang, W.; and McAuley, J. J. 2018 · 2018
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2020
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MIND: A Large-scale Dataset for News Recommendation
Wu, F.; Qiao, Y.; Chen, J.; Wu, C.; Qi, T.; Lian, J.; Liu, D.; Xie, X.; Gao, J.; Wu, W.; and Zhou, M. 2020 · 2020
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SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Mao, K.; Zhu, J.; Wang, J.; Dai, Q.; Dong, Z.; Xiao, X.; and He, X. 2021 · 2021
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Language Models as Recommender Systems: Evaluations and Limitations
Zhang, Y.; DING, H.; Shui, Z.; Ma, Y.; Zou, J.; Deoras, A.; and Wang, H. 2021 · 2021
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Calibrate Before Use: Improving Few-shot Performance of Language Models
Zhao, Z.; Wallace, E.; Feng, S.; Klein, D.; and Singh, S. 2021 · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; et al. 2022 · 2022
Cited alongside, same era.
A Survey for In-context Learning
Dong, Q.; Li, L.; Dai, D.; Zheng, C.; Wu, Z.; Chang, B.; Sun, X.; Xu, J.; and Sui, Z. 2022 · 2022
Cited alongside, same era.
A Survey on Knowledge Graph-Based Recommender Systems
Guo, Q.; Zhuang, F.; Qin, C.; Zhu, H.; Xie, X.; Xiong, H.; and He, Q. 2022 · 2022
Cited alongside, same era.
Towards Universal Sequence Representation Learning for Recommender Systems
Hou, Y.; Mu, S.; Zhao, W. X.; Li, Y.; Ding, B.; and Wen, J. 2022 · 2022
Cited alongside, same era.
Uncovering ChatGPT’s Capabilities in Recommender Systems
Dai, S.; Shao, N.; Zhao, H.; Yu, W.; Si, Z.; Xu, C.; Sun, Z.; Zhang, X.; and Xu, J. 2023 · 2023
Closest in time.
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models
Ferrara, E. 2023 · 2023
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Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Gao, Y.; Sheng, T.; Xiang, Y.; Xiong, Y.; Wang, H.; and Zhang, J. 2023 · 2023
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Large Language Models are Zero-Shot Rankers for Recommender Systems
Hou, Y.; Zhang, J.; Lin, Z.; Lu, H.; Xie, R.; McAuley, J.; and Zhao, W. X. 2023 · 2023
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Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT
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Large Language Models are Zero-Shot Reasoners
Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022
Cited alongside, same era.
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
Lu, Y.; Bartolo, M.; Moore, A.; Riedel, S.; and Stenetorp, P. 2022 · 2022
Cited alongside, same era.
Introducing ChatGPT
OpenAI. 2022 · 2022
Cited alongside, same era.
“It is just a flu”: Assessing the Effect of Watch History on YouTube’s Pseudoscientific Video Recommendations
Papadamou, K.; Zannettou, S.; Blackburn, J.; De Cristofaro, E.; Stringhini, G.; and Sirivianos, M. 2022 · 2022
Cited alongside, same era.
Dually Enhanced Propensity Score Estimation in Sequential Recommendation
Xu, C.; Xu, J.; Chen, X.; Dong, Z.; and Wen, J. 2022 · 2022
Cited alongside, same era.
Eight things to know about large language models
Bowman, S. R. 2023 · 2023
Cited alongside, same era.
Is ChatGPT a Good Recommender? A Preliminary Study
Liu, J.; Liu, C.; Zhou, P.; Lv, R.; Zhou, K.; and Zhang, Y. 2023a
Cited in the paper.
Lin, G.; and Zhang, Y. 2023 · 2023
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Qin, Z.; Jagerman, R.; Hui, K.; Zhuang, H.; Wu, J.; Shen, J.; Liu, T.; Liu, J.; Metzler, D.; Wang, X.; and Bendersky, M. 2023 · 2023
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Turpin, M.; Michael, J.; Perez, E.; and Bowman, S. R. 2023 · 2023
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Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
Wang, L.; and Lim, E.-P. 2023 · 2023
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A Survey on Large Language Models for Recommendation
Wu, L.; Zheng, Z.; Qiu, Z.; Wang, H.; Gu, H.; Shen, T.; Qin, C.; Zhu, C.; Zhu, H.; Liu, Q.; Xiong, H.; and Chen, E. 2023 · 2023
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Feedrec: News feed recommendation with various user feedbacks
Wu, C.; Wu, F.; Qi, T.; Liu, Q.; Tian, X.; Li, J.; He, W.; Huang, Y.; and Xie, X. 2022 · 2097
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