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Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence.
Language Models Are Few-Shot Learners. In Proc. NeurIPS , Vol. 33. 1877–1901
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 · 1901
Earlier work this paper cites.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 1910
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The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. In Proc. SIGIR . ACM, Melbourne Australia, 335–336
Jaime Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Accurate and diverse recommendations via eliminating redundant correlations
Tao Zhou, Ri-Qi Su, Run-Ran Liu, Luo-Luo Jiang, Bing-Hong Wang, and Yi-Cheng Zhang. 2009 · 2009
Earlier work this paper cites.
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2015 · 2015
Earlier work this paper cites.
Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures. In Proc. SIGIR . 113–122
Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. 2015 · 2015
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
Earlier work this paper cites.
Session-Based Recommendations with Recurrent Neural Networks. In Proc. ICLR
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Neural Machine Translation of Rare Words with Subword Units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Earlier work this paper cites.
Attention Is All You Need. In Proc. NeurIPS
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Self-Attentive Sequential Recommendation. In Proc. ICDM . 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Multi-task learning as multi-objective optimization. In Proc. NeurIPS
Ozan Sener and Vladlen Koltun. 2018 · 2018
Cited alongside, same era.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. In Proc. WSDM . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proc. of NAACL-HLT . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Language Models Are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
M6-Rec: Generative Pretrained Language Models Are Open-Ended Recommender Systems
Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
Later among the works it cites.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation. In Proc. CIKM . 396–405
Hanwen Du, Hui Shi, Pengpeng Zhao, Deqing Wang, Victor S. Sheng, Yanchi Liu, Guanfeng Liu, and Lei Zhao. 2022 · 2022
Later among the works it cites.
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In Proc. RecSys . 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Later among the works it cites.
DSI++: Updating Transformer Memory with New Documents
Sanket Vaibhav Mehta, Jai Gupta, Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Jinfeng Rao, Marc Najork, Emma Strubell, and Donald Metzler. 2022 · 2022
Later among the works it cites.
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Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
A Simple Convolutional Generative Network for Next Item Recommendation. In Proc. WSDM . 582–590
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, and Xiangnan He. 2019 · 2019
Cited alongside, same era.
Improving Top-K Recommendation via Joint Collaborative Autoencoders. In Proc. WWW
Ziwei Zhu, Jianling Wang, and James Caverlee. 2019 · 2019
Cited alongside, same era.
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.
Differentiable Ranking Metric Using Relaxed Sorting for Top-K Recommendation
Hyunsung Lee, Sangwoo Cho, Yeongjae Jang, Jaekwang Kim, and Honguk Woo. 2021 · 2021
Cited alongside, same era.
Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation. In Proc. RecSys . 49–58
Wei Cai, Weike Pan, Jingwen Mao, Zhechao Yu, and Congfu Xu. 2022 · 2022
Cited alongside, same era.
Effective and Efficient Training for Sequential Recommendation Using Recency Sampling. In Proc. RecSys . 81–91
Aleksandr Petrov and Craig Macdonald. 2022a
Cited in the paper.
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Later among the works it cites.
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. In Proc. WSDM . 813–823
Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022 · 2022
Later among the works it cites.
Transformer Memory as a Differentiable Search Index
Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, and Donald Metzler. 2022 · 2022
Later among the works it cites.
DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index
Yu-Jia Zhou, Jing Yao, Zhi-Cheng Dou, Ledell Wu, and Ji-Rong Wen. 2022 · 2022
Later among the works it cites.
ColBERT-FairPRF: Towards Fair Pseudo-Relevance Feedback in Dense Retrieval. In Proc. ECIR . 457–465
Thomas Jaenich, Graham McDonald, and Iadh Ounis. 2023 · 2023
Closest in time.
Recommender Systems with Generative Retrieval
Rajput, Shashank, Mehta, Nikhil, Singh, Anima, Keshavan, Raghunandan, Vu, Trung, Heldt, Lukasz, Hong, Lichan, Tay, Yi, Tran, Vinh Q., Samost, Jonah, Kula, Maciej, Chi, Ed H., and Sathiamoorthy, Maheswaran. 2023 · 2023
Closest in time.
Learning to Tokenize for Generative Retrieval
Weiwei Sun, Lingyong Yan, Zheng Chen, Shuaiqiang Wang, Haichao Zhu, Pengjie Ren, Zhumin Chen, Dawei Yin, Maarten de Rijke, and Zhaochun Ren. 2023 · 2023
Closest in time.