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Sequential recommender systems (SRS) aim to predict users' subsequent choices based on their historical interactions and have found applications in diverse fields such as e-commerce and social media.
Liii. on lines and planes of closest fit to systems of points in space
K. Pearson · 1901
Earlier work this paper cites.
An analysis for unreplicated fractional factorials
G. E. Box and R. D. Meyer · 1986
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Image-based recommendations on styles and substitutes
J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel · 2015
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk · 2016
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Self-attentive sequential recommendation
W.-C. Kang and J. McAuley · 2018
Earlier work this paper cites.
Personalized top-n sequential recommendation via convolutional sequence embedding
J. Tang and K. Wang · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. D. M.-W. C. Kenton and L. K. Toutanova · 2019
Earlier work this paper cites.
Pre-training of graph augmented transformers for medication recommendation
J. Shang, T. Ma, C. Xiao, and J. Sun · 2019
Earlier work this paper cites.
Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang · 2019
Earlier work this paper cites.
Sequential recommender systems: challenges, progress and prospects
S. Wang, L. Hu, Y. Wang, L. Cao, Q. Z. Sheng, and M. Orgun · 2019
Earlier work this paper cites.
Be your own teacher: Improve the performance of convolutional neural networks via self distillation
L. Zhang, J. Song, A. Gao, J. Chen, C. Bao, and K. Ma · 2019
Earlier work this paper cites.
Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations
H. Fang, D. Zhang, Y. Shu, and G. Guo · 2020
Earlier work this paper cites.
Cities: Contextual inference of tail-item embeddings for sequential recommendation
S. Jang, H. Lee, H. Cho, and S. Chung · 2020
Earlier work this paper cites.
Long-tail session-based recommendation
S. Liu and Y. Zheng · 2020
Earlier work this paper cites.
Sequential recommendation with graph neural networks
J. Chang, C. Gao, Y. Zheng, Y. Hui, Y. Niu, Y. Song, D. Jin, and Y. Li · 2021
Earlier work this paper cites.
Knowledge distillation: A survey
J. Gou, B. Yu, S. J. Maybank, and D. Tao · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen, et al · 2021
Earlier work this paper cites.
Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer
Z. Liu, Z. Fan, Y. Wang, and P. S. Yu · 2021
Earlier work this paper cites.
A survey on in-context learning
Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, and Z. Sui · 2022
Earlier work this paper cites.
Mlp4rec: A pure mlp architecture for sequential recommendations
M. Li, X. Zhao, C. Lyu, M. Zhao, R. Wu, and R. Guo · 2022
Earlier work this paper cites.
Sequential modeling with multiple attributes for watchlist recommendation in e-commerce
U. Singer, H. Roitman, Y. Eshel, A. Nus, I. Guy, O. Levi, I. Hasson, and E. Kiperwasser · 2022
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Filter-enhanced mlp is all you need for sequential recommendation
K. Zhou, H. Yu, W. X. Zhao, and J.-R. Wen · 2022
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Never train from scratch: Fair comparison of long-sequence models requires data-driven priors
I. Amos, J. Berant, and A. Gupta · 2023
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Tallrec: An effective and efficient tuning framework to align large language model with recommendation
K. Bao, J. Zhang, Y. Zhang, W. Wang, F. Feng, and X. He · 2023
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A unified framework for multi-domain ctr prediction via large language models
Z. Fu, X. Li, C. Wu, Y. Wang, K. Dong, X. Zhao, M. Zhao, H. Guo, and R. Tang · 2023
Cited alongside, same era.
Large language model can interpret latent space of sequential recommender
Z. Yang, J. Wu, Y. Luo, J. Zhang, Y. Yuan, A. Zhang, X. Wang, and X. He · 2023
Later among the works it cites.
A survey of large language models
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al · 2023
Later among the works it cites.
Llm2vec: Large language models are secretly powerful text encoders
P. BehnamGhader, V. Adlakha, M. Mosbach, D. Bahdanau, N. Chapados, and S. Reddy · 2024
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Llm-enhanced reranking in recommender systems
J. Gao, B. Chen, X. Zhao, W. Liu, X. Li, Y. Wang, Z. Zhang, W. Wang, Y. Ye, S. Lin, et al · 2024
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Breaking the length barrier: Llm-enhanced ctr prediction in long textual user behaviors
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Y. Gao, T. Sheng, Y. Xiang, Y. Xiong, H. Wang, and J. Zhang · 2023
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Leveraging large language models for sequential recommendation
J. Harte, W. Zorgdrager, P. Louridas, A. Katsifodimos, D. Jannach, and M. Fragkoulis · 2023
Cited alongside, same era.
Melt: Mutual enhancement of long-tailed user and item for sequential recommendation
K. Kim, D. Hyun, S. Yun, and C. Park · 2023
Cited alongside, same era.
Large language models for generative recommendation: A survey and visionary discussions
L. Li, Y. Zhang, D. Liu, and L. Chen · 2023
Cited alongside, same era.
Automlp: Automated mlp for sequential recommendations
M. Li, Z. Zhang, X. Zhao, W. Wang, M. Zhao, R. Wu, and R. Guo · 2023
Cited alongside, same era.
X. Li, C. Chen, X. Zhao, Y. Zhang, and C. Xing · 2023
Cited alongside, same era.
Mmmlp: Multi-modal multilayer perceptron for sequential recommendations
J. Liang, X. Zhao, M. Li, Z. Zhang, W. Wang, H. Liu, and Z. Liu · 2023
Cited alongside, same era.
B. Geng, Z. Huan, X. Zhang, Y. He, L. Zhang, F. Yuan, J. Zhou, and L. Mo · 2024
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Enhancing sequential recommendation via llm-based semantic embedding learning
J. Hu, W. Xia, X. Zhang, C. Fu, W. Wu, Z. Huan, A. Li, Z. Tang, and J. Zhou · 2024
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Multimodal recommender systems: A survey
Q. Liu, J. Hu, Y. Xiao, X. Zhao, J. Gao, W. Wang, Q. Li, and J. Tang · 2024
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Large language model empowered embedding generator for sequential recommendation
Q. Liu, X. Wu, W. Wang, Y. Wang, Y. Zhu, X. Zhao, F. Tian, and Y. Zheng · 2024
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When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications
Q. Liu, X. Wu, X. Zhao, Y. Zhu, D. Xu, F. Tian, and Y. Zheng · 2024
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Bidirectional gated mamba for sequential recommendation
Z. Liu, Q. Liu, Y. Wang, W. Wang, P. Jia, M. Wang, Z. Liu, Y. Chang, and X. Zhao · 2024
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Sequential recommendation for optimizing both immediate feedback and long-term retention
Z. Liu, S. Liu, Z. Zhang, Q. Cai, X. Zhao, K. Zhao, L. Hu, P. Jiang, and K. Gai · 2024
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S. Luo, Y. Yao, B. He, Y. Huang, A. Zhou, X. Zhang, Y. Xiao, M. Zhan, and L. Song · 2024
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Representation learning with large language models for recommendation
X. Ren, W. Wei, L. Xia, L. Su, S. Cheng, J. Wang, D. Yin, and C. Huang · 2024
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Language models encode collaborative signals in recommendation
L. Sheng, A. Zhang, Y. Zhang, Y. Chen, X. Wang, and T.-S. Chua · 2024
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Llm4msr: An llm-enhanced paradigm for multi-scenario recommendation
Y. Wang, Y. Wang, Z. Fu, X. Li, X. Zhao, H. Guo, and R. Tang · 2024
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Exploring large language model for graph data understanding in online job recommendations
L. Wu, Z. Qiu, Z. Zheng, H. Zhu, and E. Chen · 2024
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A survey on large language models for recommendation
L. Wu, Z. Zheng, Z. Qiu, H. Wang, H. Gu, T. Shen, C. Qin, C. Zhu, H. Zhu, Q. Liu, et al · 2024
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Ssdrec: Self-augmented sequence denoising for sequential recommendation
C. Zhang, Q. Han, R. Chen, X. Zhao, P. Tang, and H. Song · 2024
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Tired of plugins? large language models can be end-to-end recommenders
W. Zhang, X. Li, Y. Wang, K. Dong, Y. Wang, X. Dai, X. Zhao, H. Guo, R. Tang, et al · 2024
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Recommender systems in the era of large language models (llms)
Z. Zhao, W. Fan, J. Li, Y. Liu, X. Mei, Y. Wang, Z. Wen, F. Wang, X. Zhao, J. Tang, et al · 2024
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Harnessing large language models for text-rich sequential recommendation
Z. Zheng, W. Chao, Z. Qiu, H. Zhu, and H. Xiong · 2024
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