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Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs).
Language models are few-shot learners
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Very deep convolutional networks for large-scale image recognition
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Session-based recommendations with recurrent neural networks
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Image-based recommendations on styles and substitutes
McAuley, J.; Targett, C.; Shi, Q.; and Van Den Hengel, A. 2015 · 2015
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R.; and McAuley, J. 2016 · 2016
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Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Monti, F.; Bronstein, M.; and Bresson, X. 2017 · 2017
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Multimodal Machine Learning: A Survey and Taxonomy
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Self-attentive sequential recommendation
Kang, W.-C.; and McAuley, J. 2018 · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Tang, J.; and Wang, K. 2018 · 2018
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Multiple pairwise ranking with implicit feedback
Yu, R.; Zhang, Y.; Ye, Y.; Wu, L.; Wang, C.; Liu, Q.; and Chen, E. 2018 · 2018
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A Survey on Deep Learning Based Recommender Systems: From Traditional Methods to Recent Advances in Multimedia Recommendations
Huang, Z.; Xiao, W.; and Yu, Y. 2019 · 2019
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Tissa: A time slice self-attention approach for modeling sequential user behaviors
Lei, C.; Ji, S.; and Li, Z. 2019 · 2019
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DIF-SR: Differential Sequential Recommendation
Rendle, S.; Krichene, W.; Zhang, L.; and Anderson, J. 2019 · 2019
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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
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MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
Wei, Y.; Zhao, X.; Liu, G.; Zhu, Z.; Zhuang, Y.; Qin, J.; and Wang, J. 2019 · 2019
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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
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GPT-3: Its nature, scope, limits, and consequences
Floridi, L.; and Chiriatti, M. 2020 · 2020
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A survey on deep learning for multimodal data fusion
Gao, J.; Li, P.; Chen, Z.; and Zhang, J. 2020 · 2020
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Two-stage sequential recommendation via bidirectional attentive behavior embedding and long/short-term integration
Ji, W.; Sun, Y.; Chen, T.; and Wang, X. 2020 · 2020
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Collaborative list-and-pairwise filtering from implicit feedback
Li, X.; Chen, C.; Zhao, X.; Zhang, Y.; and Xing, C. 2023 · 2023
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A Content-Driven Micro-Video Recommendation Dataset at Scale
Ni, Y.; Cheng, Y.; Liu, X.; Fu, J.; Li, Y.; He, X.; Zhang, Y.; and Yuan, F. 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
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A Survey on Large Language Models for Recommendation
Wu, L.; Zheng, Z.; Qiu, Z.; Wang, H.; Shen, T.; Qin, C.; Zhu, C.; Zhu, H.; Liu, Q.; et al. 2023 · 2023
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Yu, R.; Liu, Q.; Ye, Y.; Cheng, M.; Chen, E.; and Ma, J. 2020 · 2020
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CLIP: Connecting Text and Images
Gao, P.; Zareian, A.; Maji, S.; Darrell, T.; and Rohrbach, M. 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
Cited alongside, same era.
Language models are realistic tabular data generators
Borisov, V.; Seßler, K.; Leemann, T.; Pawelczyk, M.; and Kasneci, G. 2022 · 2022
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M6-rec: Generative pretrained language models are open-ended recommender systems
Cui, Z.; Ma, J.; Zhou, C.; Zhou, J.; and Yang, H. 2022 · 2022
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Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt, and Predict Paradigm (P5)
Geng, S.; Liu, S.; Fu, Z.; Ge, Y.; and Zhang, Y. 2022 · 2022
Cited alongside, same era.
Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023 · 2023
Cited alongside, same era.
Heterogeneous knowledge fusion: A novel approach for personalized recommendation via llm
Yin, B.; Xie, J.; Qin, Y.; Ding, Z.; Feng, Z.; Li, X.; and Lin, W. 2023 · 2023
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Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Yu, R.; Xu, X.; Ye, Y.; Liu, Q.; and Chen, E. 2023 · 2023
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Knowledge prompt-tuning for sequential recommendation
Zhai, J.; Zheng, X.; Wang, C.-D.; Li, H.; and Tian, Y. 2023 · 2023
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Aligning Large Language Models with Recommendation Knowledge
Cao, Y.; Mehta, N.; Yi, X.; Keshavan, R.; Heldt, L.; Hong, L.; Chi, E. H.; and Sathiamoorthy, M. 2024 · 2024
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Scaling New Frontiers: Insights into Large Recommendation Models
Guo, W.; Wang, H.; Zhang, L.; Chin, J. Y.; Liu, Z.; Cheng, K.; Pan, Q.; Lee, Y. Q.; Xue, W.; Shen, T.; et al. 2024 · 2024
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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
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MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
Kim, Y.; Kim, T.; Shin, W.-Y.; and Kim, S.-W. 2024 · 2024
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Generating images with multimodal language models
Koh, J. Y.; Fried, D.; and Salakhutdinov, R. R. 2024 · 2024
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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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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Yue, X.; Ni, Y.; Zhang, K.; Zheng, T.; Liu, R.; Zhang, G.; Stevens, S.; Jiang, D.; Ren, W.; Sun, Y.; et al. 2024 · 2024
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NoteLLM-2: Multimodal Large Representation Models for Recommendation
Zhang, C.; Zhang, H.; Wu, S.; Wu, D.; Xu, T.; Gao, Y.; Hu, Y.; and Chen, E. 2024 · 2024
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Harnessing Large Language Models for Text-Rich Sequential Recommendation
Zheng, Z.; Chao, W.; Qiu, Z.; Zhu, H.; and Xiong, H. 2024 · 2024
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Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation With Interpretability
Zhou, X.; and Miao, C. 2024 · 2024
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