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Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences.
Adaptive mixtures of local experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton · 1991
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Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec ’11, Chicago, Illinois, USA, October 27, 2011 , 2011. ACM
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik, editors · 2011
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The movielens datasets: History and context
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk · 2016
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
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Personalized top-n sequential recommendation via convolutional sequence embedding
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Sequential recommender systems: Challenges, progress and prospects
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BERT: pre-training of deep bidirectional transformers for language understanding
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models
Zirui Wang, Yulia Tsvetkov, Orhan Firat, and Yuan Cao · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
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Gshard: Scaling giant models with conditional computation and automatic sharding
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush · 2022
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Towards a unified view of parameter-efficient transfer learning
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Navigating text-to-image customization: From lycoris fine-tuning to model evaluation
Shin-Ying Yeh, Yu-Guan Hsieh, Zhidong Gao, Bernard B. W. Yang, Giyeong Oh, and Yanmin Gong · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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A causality inspired framework for model interpretation
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Improving recommendation fairness via data augmentation
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin Raffel · 2022
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Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Kumar Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Schärli, Aakanksha Chowdhery, Philip Andrew Mansfield, Blaise Agüera y Arcas, Dale R. Webster, Gregory S. Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle K. Barral, Christopher Semturs, Alan Karthikesalingam, and Vivek Natarajan · 2022
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Uncovering chatgpt’s capabilities in recommender systems
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Llara: Aligning large language models with sequential recommenders
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Customizable combination of parameter-efficient modules for multi-task learning
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Chatlaw: Open-source legal large language model with integrated external knowledge bases
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Task-customized masked autoencoder via mixture of cluster-conditional experts
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Scaling vision-language models with sparse mixture of experts
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A survey on large language models for recommendation
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Large language models are zero-shot rankers for recommender systems
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LLM4DSR: leveraing large language model for denoising sequential recommendation
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Distillation matters: Empowering sequential recommenders to match the performance of large language models
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On softmax direct preference optimization for recommendation
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Language models encode collaborative signals in recommendation
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CIRS: bursting filter bubbles by counterfactual interactive recommender system
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