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Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems (RecSys) are three prominent AI applications that have traditionally developed independently, resulting in disparate modeling and engineering methodologies.
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 1902
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Explainable fashion recommendation: A semantic attribute region guided approach
Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W Zheng, and Qi Liu. 2019 · 1905
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Unifying vision-and-language tasks via text generation
Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. 2021 · 1942
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Bpr: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
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Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification
Yongfeng Zhang, Haochen Zhang, Min Zhang, Yiqun Liu, and Shaoping Ma. 2014 · 2014
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
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Vbpr: visual bayesian personalized ranking from implicit feedback
Ruining He and Julian McAuley. 2016 · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Learning to generate product reviews from attributes
Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. 2017 · 2017
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Neural rating regression with abstractive tips generation for recommendation
Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017 · 2017
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Joint representation learning for top-n recommendation with heterogeneous information sources
Yongfeng Zhang, Qingyao Ai, Xu Chen, and W Bruce Croft. 2017 · 2017
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
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Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation
Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019 · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Hierarchical gating networks for sequential recommendation
Chen Ma, Peng Kang, and Xue Liu. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris 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 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Heterogeneous fusion of semantic and collaborative information for visually-aware food recommendation
Lei Meng, Fuli Feng, Xiangnan He, Xiaoyan Gao, and Tat-Seng Chua. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Path language modeling over knowledge graphsfor explainable recommendation
Shijie Geng, Zuohui Fu, Juntao Tan, Yingqiang Ge, Gerard De Melo, and Yongfeng Zhang. 2022b · 2022
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Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. 2022 · 2022
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Vima: General robot manipulation with multimodal prompts
Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, and Linxi Fan. 2022 · 2022
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Personalized prompt learning for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2022 · 2022
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Reframing instructional prompts to gptk’s language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. 2022 · 2022
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Cited alongside, same era.
Fashionist: Personalising outfit recommendation for cold-start scenarios
Dhruv Verma, Kshitij Gulati, Vasu Goel, and Rajiv Ratn Shah. 2020 · 2020
Cited alongside, same era.
Learning from task descriptions
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew E Peters. 2020 · 2020
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Personalized transformer for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen 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 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 Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022 · 2022
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Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal. 2022 · 2022
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OFA: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework
Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
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Coca: Contrastive captioners are image-text foundation models
Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. 2022 · 2022
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022
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A revisiting study of appropriate offline evaluation for top-n recommendation algorithms
Wayne Xin Zhao, Zihan Lin, Zhichao Feng, Pengfei Wang, and Ji-Rong Wen. 2022 · 2022
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022 · 2022
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OpenAGI: When LLM Meets Domain Experts
Yingqiang Ge, Wenyue Hua, Kai Mei, Jianchao Ji, Juntao Tan, Shuyuan Xu, Zelong Li, and Yongfeng Zhang. 2023 · 2023
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Yunqi Li and Yongfeng Zhang. 2023 · 2023
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Sparks of Artificial General Recommender (AGR): Experiments with ChatGPT
Guo Lin and Yongfeng Zhang. 2023 · 2023
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OpenP5: Benchmarking Foundation Models for Recommendation
Shuyuan Xu, Wenyue Hua, and Yongfeng Zhang. 2023 · 2023
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Natural Language is All a Graph Needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang. 2023 · 2023
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