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Large Language Models (LLMs) have demonstrated unprecedented language understanding and reasoning capabilities to capture diverse user preferences and advance personalized recommendations.
C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” in Proceedings of the 14th international conference on World Wide Web , 2005, pp. 22–32
2005
Earlier work this paper cites.
F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” Acm transactions on interactive intelligent systems (tiis) , vol. 5, no. 4, pp. 1–19, 2015
2015
Earlier work this paper cites.
2018
Earlier work this paper cites.
W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in The world wide web conference , 2019, pp. 417–426
2019
Earlier work this paper cites.
W. Fan, Y. Ma, Q. Li, J. Wang, G. Cai, J. Tang, and D. Yin, “A graph neural network framework for social recommendations,” IEEE Transactions on Knowledge and Data Engineering , vol. 34, no. 5, pp. 2033–2047, 2020
2020
Earlier work this paper cites.
M. Wan, J. Ni, R. Misra, and J. McAuley, “Addressing marketing bias in product recommendations,” in Proceedings of the 13th international conference on web search and data mining , 2020, pp. 618–626
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020
2020
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 3045–3059
2021
Earlier work this paper cites.
Y. Guo, Y. Yang, and A. Abbasi, “Auto-debias: Debiasing masked language models with automated biased prompts,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 1012–1023
2022
Earlier work this paper cites.
X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang, “P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , 2022, pp. 61–68
2022
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models.” ICLR , vol. 1, no. 2, p. 3, 2022
2022
Earlier work this paper cites.
R. Navigli, S. Conia, and B. Ross, “Biases in large language models: origins, inventory, and discussion,” ACM Journal of Data and Information Quality , vol. 15, no. 2, pp. 1–21, 2023
2023
Earlier work this paper cites.
H. Kotek, R. Dockum, and D. Sun, “Gender bias and stereotypes in large language models,” in Proceedings of the ACM collective intelligence conference , 2023, pp. 12–24
2023
Earlier work this paper cites.
Y. Wang, W. Ma, M. Zhang, Y. Liu, and S. Ma, “A survey on the fairness of recommender systems,” ACM Transactions on Information Systems , vol. 41, no. 3, pp. 1–43, 2023
2023
Earlier work this paper cites.
J. Chen, H. Dong, X. Wang, F. Feng, M. Wang, and X. He, “Bias and debias in recommender system: A survey and future directions,” ACM Transactions on Information Systems , vol. 41, no. 3, pp. 1–39, 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
J. Zhang, K. Bao, Y. Zhang, W. Wang, F. Feng, and X. He, “Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation,” in Proceedings of the 17th ACM Conference on Recommender Systems , 2023, pp. 993–999
2023
Cited alongside, same era.
W. Hua, S. Xu, Y. Ge, and Y. Zhang, “How to index item ids for recommendation foundation models,” in Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region , 2023, pp. 195–204
2023
Cited alongside, same era.
H. Rao, C. Leung, and C. Miao, “Can chatgpt assess human personalities? a general evaluation framework,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 1184–1194
2023
Cited alongside, same era.
2023
2024
Later among the works it cites.
2024
Later among the works it cites.
K. Bao, J. Zhang, X. Lin, Y. Zhang, W. Wang, and F. Feng, “Large language models for recommendation: Past, present, and future,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2024, pp. 2993–2996
2024
Later among the works it cites.
Y. Wang, P. Sun, W. Ma, M. Zhang, Y. Zhang, P. Jiang, and S. Ma, “Intersectional two-sided fairness in recommendation,” in Proceedings of the ACM on Web Conference 2024 , 2024, pp. 3609–3620
2024
Later among the works it cites.
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Cited alongside, same era.
2023
Cited alongside, same era.
J.-Y. Choi, J. Kim, J.-H. Park, W.-L. Mok, and S. Lee, “Smop: Towards efficient and effective prompt tuning with sparse mixture-of-prompts,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 14 306–14 316
2023
Cited alongside, same era.
2023
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 Proceedings of the 17th ACM Conference on Recommender Systems , 2023, pp. 1007–1014
2023
Cited alongside, same era.
V. R. Vuyyala, M. S. R. Kona, S. B. Pusuluri, S. Variganji, and B. Nenavathu, “Crop recommender system based on ensemble classifiers,” in 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) . IEEE, 2023, pp. 68–73
2023
Cited alongside, same era.
K. Yang, C. Yu, Y. R. Fung, M. Li, and H. Ji, “Adept: A debiasing prompt framework,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 9, 2023, pp. 10 780–10 788
2023
Cited alongside, same era.
Z. Liu, C. Zhang, Y. Tian, E. Zhang, C. Huang, Y. Ye, and C. Zhang, “Fair graph representation learning via diverse mixture-of-experts,” in Proceedings of the ACM Web Conference 2023 , 2023, pp. 28–38
2023
Cited alongside, same era.
W. Jin, H. Mao, Z. Li, H. Jiang, C. Luo, H. Wen, H. Han, H. Lu, Z. Wang, R. Li et al. , “Amazon-m2: A multilingual multi-locale shopping session dataset for recommendation and text generation,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Zhu, L. Wu, Q. Guo, L. Hong, and J. Li, “Collaborative large language model for recommender systems,” in Proceedings of the ACM on Web Conference 2024 , 2024, pp. 3162–3172
2024
Later among the works it cites.
2024
Later among the works it cites.
S. Dai, C. Xu, S. Xu, L. Pang, Z. Dong, and J. Xu, “Bias and unfairness in information retrieval systems: New challenges in the llm era,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 6437–6447
2024
Later among the works it cites.
2024
Later among the works it cites.
A. Tommasel, “Fairness matters: A look at llm-generated group recommendations,” in Proceedings of the 18th ACM Conference on Recommender Systems , 2024, pp. 993–998
2024
Later among the works it cites.
Y. Tian, F. Xia, and Y. Song, “Dialogue summarization with mixture of experts based on large language models,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2024, pp. 7143–7155
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Xi, W. Liu, J. Lin, X. Cai, H. Zhu, J. Zhu, B. Chen, R. Tang, W. Zhang, and Y. Yu, “Towards open-world recommendation with knowledge augmentation from large language models,” in Proceedings of the 18th ACM Conference on Recommender Systems , 2024, pp. 12–22
2024
Later among the works it cites.