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Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items.
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
Zhou, K.; Wang, H.; Zhao, W. X.; Zhu, Y.; Wang, S.; Zhang, F.; Wang, Z.; and Wen, J.-R. 2020 · 1902
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
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Session-based recommendations with recurrent neural networks
Hidasi, B.; Karatzoglou, A.; Baltrunas, L.; and Tikk, D. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Earlier work this paper cites.
Self-attentive sequential recommendation
Kang, W.-C.; and McAuley, J. 2018 · 2018
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Ni, J.; Li, J.; and McAuley, J. 2019 · 2019
Earlier work this paper cites.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer
Sun, F.; Liu, J.; Wu, J.; Pei, C.; Lin, X.; Ou, W.; and Jiang, P. 2019 · 2019
Earlier work this paper cites.
Feature-level deeper self-attention network for sequential recommendation
Zhang, T.; Zhao, P.; Liu, Y.; Sheng, V. S.; Xu, J.; Wang, D.; Liu, G.; Zhou, X.; et al. 2019 · 2019
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
Earlier work this paper cites.
Mind: A large-scale dataset for news recommendation
Wu, F.; Qiao, Y.; Chen, J.-H.; Wu, C.; Qi, T.; Lian, J.; Liu, D.; Xie, X.; Gao, J.; Wu, W.; et al. 2020 · 2020
Earlier work this paper cites.
Ding, H.; Ma, Y.; Deoras, A.; Wang, Y.; and Wang, H. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
Cited alongside, same era.
Towards universal sequence representation learning for recommender systems
Hou, Y.; Mu, S.; Zhao, W. X.; Li, Y.; Ding, B.; and Wen, J.-R. 2022 · 2022
Cited alongside, same era.
RecGURU: Adversarial learning of generalized user representations for cross-domain recommendation
Li, C.; Zhao, M.; Zhang, H.; Yu, C.; Cheng, L.; Shu, G.; Kong, B.; and Niu, D. 2022 · 2022
Cited alongside, same era.
PinnerFormer: Sequence Modeling for User Representation at Pinterest
Pancha, N.; Zhai, A.; Leskovec, J.; and Rosenberg, C. 2022 · 2022
Cited alongside, same era.
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
Yang, A.; Pan, J.; Lin, J.; Men, R.; Zhang, Y.; Zhou, J.; and Zhou, C. 2022 · 2022
Cited alongside, same era.
Is chatgpt a good recommender? a preliminary study
Liu, J.; Liu, C.; Lv, R.; Zhou, K.; and Zhang, Y. 2023 · 2023
Later among the works it cites.
Llm-rec: Personalized recommendation via prompting large language models
Lyu, H.; Jiang, S.; Zeng, H.; Xia, Y.; and Luo, J. 2023 · 2023
Later among the works it cites.
Representation learning with large language models for recommendation
Ren, X.; Wei, W.; Xia, L.; Su, L.; Cheng, S.; Wang, J.; Yin, D.; and Huang, C. 2023 · 2023
Later among the works it cites.
Large language models are competitive near cold-start recommenders for language-and item-based preferences
Sanner, S.; Balog, K.; Radlinski, F.; Wedin, B.; and Dixon, L. 2023 · 2023
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Missrec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation
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Baichuan 2: Open Large-scale Language Models
Baichuan. 2023 · 2023
Cited alongside, same era.
TBIN: Modeling Long Textual Behavior Data for CTR Prediction
Chen, S.; Li, X.; Dong, J.; Zhang, J.; Wang, Y.; and Wang, X. 2023 · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; et al. 2023 · 2023
Cited alongside, same era.
Recommender systems in the era of large language models (llms)
Fan, W.; Zhao, Z.; Li, J.; Liu, Y.; Mei, X.; Wang, Y.; Tang, J.; and Li, Q. 2023 · 2023
Cited alongside, same era.
Chat-rec: Towards interactive and explainable llms-augmented recommender system
Gao, Y.; Sheng, T.; Xiang, Y.; Xiong, Y.; Wang, H.; and Zhang, J. 2023 · 2023
Cited alongside, same era.
Text is all you need: Learning language representations for sequential recommendation
Li, J.; Wang, M.; Li, J.; Fu, J.; Shen, X.; Shang, J.; and McAuley, J. 2023 · 2023
Cited alongside, same era.
Prompt distillation for efficient llm-based recommendation
Li, L.; Zhang, Y.; and Chen, L. 2023 · 2023
Cited alongside, same era.
Wang, J.; Zeng, Z.; Wang, Y.; Wang, Y.; Lu, X.; Li, T.; Yuan, J.; Zhang, R.; Zheng, H.-T.; and Xia, S.-T. 2023 · 2023
Later among the works it cites.
Towards open-world recommendation with knowledge augmentation from large language models
Xi, Y.; Liu, W.; Lin, J.; Zhu, J.; Chen, B.; Tang, R.; Zhang, W.; Zhang, R.; and Yu, Y. 2023 · 2023
Later among the works it cites.
Where to go next for recommender systems? id-vs. modality-based recommender models revisited
Yuan, Z.; Yuan, F.; Song, Y.; Li, Y.; Fu, J.; Yang, F.; Pan, Y.; and Ni, Y. 2023 · 2023
Later among the works it cites.
LlamaRec: Two-stage recommendation using large language models for ranking
Yue, Z.; Rabhi, S.; Moreira, G. d. S. P.; Wang, D.; and Oldridge, E. 2023 · 2023
Later among the works it cites.
Large language models are zero-shot rankers for recommender systems
Hou, Y.; Zhang, J.; Lin, Z.; Lu, H.; Xie, R.; McAuley, J.; and Zhao, W. X. 2024 · 2024
Closest in time.
Representation learning with large language models for recommendation
Ren, X.; Wei, W.; Xia, L.; Su, L.; Cheng, S.; Wang, J.; Yin, D.; and Huang, C. 2024 · 2024
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Sheng, X.-R.; Yang, F.; Gong, L.; Wang, B.; Chan, Z.; Zhang, Y.; Cheng, Y.; Zhu, Y.-N.; Ge, T.; Zhu, H.; et al. 2024 · 2024
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RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm
Zhang, Y.; Yu, W.; Zhang, E.; Chen, X.; Hu, L.; Jiang, P.; and Gai, K. 2024 · 2024
Closest in time.