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Recently, sequential recommendation has been adapted to the LLM paradigm to enjoy the power of LLMs.
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, et al · 1901
Earlier work this paper cites.
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM international conference on information & knowledge management . 1893–1902
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 1902
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
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.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th international conference on data mining (ICDM) . IEEE, 191–200
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM conference on recommender systems . 241–248
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Context-aware sequential recommendation. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 1053–1058
Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, and Liang Wang. 2016 · 2016
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior.. In IJCAI . 5264–5268
Ruining He, Wang-Cheng Kang, Julian J McAuley, et al · 2018
Earlier work this paper cites.
Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM international conference on information and knowledge management . 843–852
Balázs Hidasi and Alexandros Karatzoglou. 2018 · 2018
Earlier work this paper cites.
Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
A contextual attention recurrent architecture for context-aware venue recommendation. In The 41st international ACM SIGIR conference on research & development in information retrieval . 555–564
Jarana Manotumruksa, Craig Macdonald, and Iadh Ounis. 2018 · 2018
Earlier work this paper cites.
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Earlier work this paper cites.
Taxonomy-aware multi-hop reasoning networks for sequential recommendation. In Proceedings of the twelfth ACM international conference on web search and data mining . 573–581
Jin Huang, Zhaochun Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, and Daxiang Dong. 2019 · 2019
Earlier work this paper cites.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) . 188–197
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management . 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 346–353
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019 · 2019
Cited alongside, same era.
CosRec: 2D convolutional neural networks for sequential recommendation. In Proceedings of the 28th ACM international conference on information and knowledge management . 2173–2176
An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, and Julian McAuley. 2019 · 2019
Cited alongside, same era.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023 · 2023
Later among the works it cites.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, and Julian McAuley. 2023c · 2023
Later among the works it cites.
Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, Jiaqi Zhang, and Fajie Yuan. 2023b · 2023
Later among the works it cites.
Xinhang Li, Chong Chen, Xiangyu Zhao, Yong Zhang, and Chunxiao Xing. 2023a · 2023
Later among the works it cites.
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Feature-level Deeper Self-Attention Network for Sequential Recommendation.. In IJCAI . 4320–4326
Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, Xiaofang Zhou, et al · 2019
Cited alongside, same era.
Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2020
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.
Attention-based context-aware sequential recommendation model
Weihua Yuan, Hong Wang, Xiaomei Yu, Nan Liu, and Zhenghao Li. 2020 · 2020
Cited alongside, same era.
Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, and Hao Wang. 2021 · 2021
Cited alongside, same era.
U-BERT: Pre-training user representations for improved recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 4320–4327
Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan. 2021 · 2021
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems . 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
Towards universal sequence representation learning for recommender systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 585–593
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022 · 2022
Cited alongside, same era.
Is chatgpt a good recommender? a preliminary study
Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023 · 2023
Later among the works it cites.
LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation
Kai Mei and Yongfeng Zhang. 2023 · 2023
Later among the works it cites.
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation
Junyan Qiu, Haitao Wang, Zhaolin Hong, Yiping Yang, Qiang Liu, and Xingxing Wang. 2023 · 2023
Later among the works it cites.
Recommender Systems with Generative Retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q Tran, Jonah Samost, et al · 2023
Later among the works it cites.
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
Later among the works it cites.
Large language models can accurately predict searcher preferences
Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Large Language Model Can Interpret Latent Space of Sequential Recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, and Xiangnan He. 2023 · 2023
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Where to go next for recommender systems? id-vs. modality-based recommender models revisited
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
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LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang, and Even Oldridge. 2023 · 2023
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Recommendation as instruction following: A large language model empowered recommendation approach
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023 · 2023
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