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2023

Recommender Systems in the Era of Large Language Models (LLMs)

Zhao, Zihuai, Fan, Wenqi, Li, Jiatong et al.

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With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component of our daily life, providing personalized suggestions that cater to user preferences.

While Deep Neural Networks (DNNs) have made significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating textual side information, DNN-based methods still face limitations, such as difficulties in understanding users' interests and capturing textual side information, inabilities in generalizing to various recommendation scenarios and reasoning on their predictions, etc.
Meanwhile, the emergence of Large Language Models (LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization and reasoning capabilities.
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.

Recommender Systems in the Era of Large Language Models (LLMs) · Around

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2023

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S. Bills, N. Cammarata, D. Mossing, H. Tillman, L. Gao, G. Goh, I. Sutskever, J. Leike, J. Wu, and W. Saunders, “Language models can explain neurons in language models,” URL https://openaipublic. blob. core. windows. net/neuron-explainer/paper/index. html.(Date accessed: 14.05. 2023) , 2023

2023

Closest in time.

Y. Li, Z. Tan, and Y. Liu, “Privacy-preserving prompt tuning for large language model services,” arXiv preprint arXiv:2305.06212 , 2023

2023

Closest in time.

Y. Tong, Y. Zeng, Z. Zhou, B. Liu, Y. Shi, S. Li, K. Xu, and W. Lv, “Federated computing: Query, learning, and beyond,” 2023

2023

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X. L. Dong, S. Moon, Y. E. Xu, K. Malik, and Z. Yu, “Towards next-generation intelligent assistants leveraging llm techniques,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023, pp. 5792–5793

2023

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W. Zhuang, C. Chen, and L. Lyu, “When foundation model meets federated learning: Motivations, challenges, and future directions,” arXiv preprint arXiv:2306.15546 , 2023

2023

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A. J. Nastasi, K. R. Courtright, S. D. Halpern, and G. E. Weissman, “Does chatgpt provide appropriate and equitable medical advice?: A vignette-based, clinical evaluation across care contexts,” medRxiv , pp. 2023–02, 2023

2023

Closest in time.

H. Zhang, J. Chen, F. Jiang, F. Yu, Z. Chen, J. Li, G. Chen, X. Wu, Z. Zhang, Q. Xiao et al. , “Huatuogpt, towards taming language model to be a doctor,” arXiv preprint arXiv:2305.15075 , 2023

2023

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H. Xiong, S. Wang, Y. Zhu, Z. Zhao, Y. Liu, Q. Wang, and D. Shen, “Doctorglm: Fine-tuning your chinese doctor is not a herculean task,” arXiv preprint arXiv:2304.01097 , 2023

Original

2023

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H.-T. Nguyen, “A brief report on lawgpt 1.0: A virtual legal assistant based on gpt-3,” arXiv preprint arXiv:2302.05729 , 2023

2023

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Q. Huang, M. Tao, Z. An, C. Zhang, C. Jiang, Z. Chen, Z. Wu, and Y. Feng, “Lawyer llama technical report,” arXiv preprint arXiv:2305.15062 , 2023

2023

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H. Yang, X.-Y. Liu, and C. D. Wang, “Fingpt: Open-source financial large language models,” arXiv preprint arXiv:2306.06031 , 2023

2023

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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,” arXiv preprint arXiv:2307.09688 , 2023

2023

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J. Fu, F. Yuan, Y. Song, Z. Yuan, M. Cheng, S. Cheng, J. Zhang, J. Wang, and Y. Pan, “Exploring adapter-based transfer learning for recommender systems: Empirical studies and practical insights,” arXiv preprint arXiv:2305.15036 , 2023

2023

Closest in time.