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In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences.
State-space models
Hamilton, J. D. 1994 · 1994
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Sequential recommender systems: challenges, progress and prospects
Wang, S.; Hu, L.; Wang, Y.; Cao, L.; Sheng, Q. Z.; and Orgun, M. 2019 · 2001
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Session-based recommendations with recurrent neural networks
Hidasi, B.; Karatzoglou, A.; Baltrunas, L.; and Tikk, D. 2015 · 2015
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When recurrent neural networks meet the neighborhood for session-based recommendation
Jannach, D.; and Ludewig, M. 2017 · 2017
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Neural attentive session-based recommendation
Li, J.; Ren, P.; Chen, Z.; Ren, Z.; Lian, T.; and Ma, J. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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ReLU deep neural networks and linear finite elements
He, J.; Li, L.; Xu, J.; and Zheng, C. 2018 · 2018
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Self-attentive sequential recommendation
Kang, W.-C.; and McAuley, J. 2018 · 2018
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Activation functions: Comparison of trends in practice and research for deep learning
Nwankpa, C.; Ijomah, W.; Gachagan, A.; and Marshall, S. 2018 · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z.; and Sabuncu, M. 2018 · 2018
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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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A simple convolutional generative network for next item recommendation
Yuan, F.; Karatzoglou, A.; Arapakis, I.; Jose, J. M.; and He, X. 2019 · 2019
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Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations
Fang, H.; Zhang, D.; Shu, Y.; and Guo, G. 2020 · 2020
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Time to Shop for Valentine’s Day: Shopping Occasions and Sequential Recommendation in E-commerce
Wang, J.; Louca, R.; Hu, D.; Cellier, C.; Caverlee, J.; and Hong, L. 2020 · 2020
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Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation
de Souza Pereira Moreira, G.; Rabhi, S.; Lee, J. M.; Ak, R.; and Oldridge, E. 2021 · 2021
Cited alongside, same era.
Bidirectional distillation for top-K recommender system
Kweon, W.; Kang, S.; and Yu, H. 2021 · 2021
Cited alongside, same era.
Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms
Zhao, W. X.; Mu, S.; Hou, Y.; Lin, Z.; Chen, Y.; Pan, X.; Li, K.; Lu, Y.; Wang, H.; Tian, C.; et al. 2021 · 2021
Cited alongside, same era.
Gromov-wasserstein guided representation learning for cross-domain recommendation
Li, X.; Qiu, Z.; Zhao, X.; Wang, Z.; Zhang, Y.; Xing, C.; and Wu, X. 2022 · 2022
Cited alongside, same era.
AdaFS: Adaptive feature selection in deep recommender system
Lin, W.; Zhao, X.; Wang, Y.; Xu, T.; and Wu, X. 2022 · 2022
Cited alongside, same era.
Multi-task recommendations with reinforcement learning
Liu, Z.; Tian, J.; Cai, Q.; Zhao, X.; Gao, J.; Liu, S.; Chen, D.; He, T.; Zheng, D.; Jiang, P.; et al. 2023c · 2023
Later among the works it cites.
Multi-task deep recommender systems: A survey
Wang, Y.; Lam, H. T.; Wong, Y.; Liu, Z.; Zhao, X.; Wang, Y.; Chen, B.; Guo, H.; and Tang, R. 2023 · 2023
Later among the works it cites.
Evolution of deep learning-based sequential recommender systems: from current trends to new perspectives
Yoon, J. H.; and Jang, B. 2023 · 2023
Later among the works it cites.
Denoising and prompt-tuning for multi-behavior recommendation
Zhang, C.; Chen, R.; Zhao, X.; Han, Q.; and Li, L. 2023 · 2023
Later among the works it cites.
Griffin: Mixing gated linear recurrences with local attention for efficient language models
De, S.; Smith, S. L.; Fernando, A.; Botev, A.; Cristian-Muraru, G.; Gu, A.; Haroun, R.; Berrada, L.; Chen, Y.; Srinivasan, S.; et al. 2024 · 2024
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Smith, J. T.; Warrington, A.; and Linderman, S. W. 2022 · 2022
Cited alongside, same era.
Autoassign: Automatic shared embedding assignment in streaming recommendation
Song, F.; Chen, B.; Zhao, X.; Guo, H.; and Tang, R. 2022 · 2022
Cited alongside, same era.
Hierarchical item inconsistency signal learning for sequence denoising in sequential recommendation
Zhang, C.; Du, Y.; Zhao, X.; Han, Q.; Chen, R.; and Li, L. 2022 · 2022
Cited alongside, same era.
Frequency enhanced hybrid attention network for sequential recommendation
Du, X.; Yuan, H.; Zhao, P.; Qu, J.; Zhuang, F.; Liu, G.; Liu, Y.; and Sheng, V. S. 2023 · 2023
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Gu, A.; and Dao, T. 2023 · 2023
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On the computational complexity of self-attention
Keles, F. D.; Wijewardena, P. M.; and Hegde, C. 2023 · 2023
Cited alongside, same era.
Automlp: Automated mlp for sequential recommendations
Li, M.; Zhang, Z.; Zhao, X.; Wang, W.; Zhao, M.; Wu, R.; and Guo, R. 2023b · 2023
Cited alongside, same era.
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SMLP4Rec: An Efficient all-MLP Architecture for Sequential Recommendations
Gao, J.; Zhao, X.; Li, M.; Zhao, M.; Wu, R.; Guo, R.; Liu, Y.; and Yin, D. 2024 · 2024
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Jiang, X.; Han, C.; and Mesgarani, N. 2024 · 2024
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Hierarchically gated recurrent neural network for sequence modeling
Qin, Z.; Yang, S.; and Zhong, Y. 2024 · 2024
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Rethinking large language model architectures for sequential recommendations
Wang, H.; Liu, X.; Fan, W.; Zhao, X.; Kini, V.; Yadav, D.; Wang, F.; Wen, Z.; Tang, J.; and Liu, H. 2024 · 2024
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Wang, Y.; He, X.; and Zhu, S. 2024 · 2024
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Uncovering Selective State Space Model’s Capabilities in Lifelong Sequential Recommendation
Yang, J.; Li, Y.; Zhao, J.; Wang, H.; Ma, M.; Ma, J.; Ren, Z.; Zhang, M.; Xin, X.; Chen, Z.; et al. 2024 · 2024
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There is HOPE to Avoid HiPPOs for Long-memory State Space Models
Yu, A.; Mahoney, M. W.; and Erichson, N. B. 2024 · 2024
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GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
Zhang, S.; Wang, M.; and Zhao, X. 2024 · 2024
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