Fetching the paper…
Reading the bibliography…
In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhanced sequential recommendation (SR) methods.
Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
Earlier work this paper cites.
Well-read students learn better: On the importance of pre-training compact models
Turc, I.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 1908
Earlier work this paper cites.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V.; Debut, L.; Chaumond, J.; and Wolf, T. 2019 · 1910
Earlier work this paper cites.
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.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R.; and McAuley, J. 2016 · 2016
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N.; Mirhoseini, A.; Maziarz, K.; Davis, A.; Le, Q.; Hinton, G.; and Dean, J. 2017 · 2017
Earlier work this paper cites.
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
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
Earlier work this paper cites.
Personalized top-n sequential recommendation via convolutional sequence embedding
Tang, J.; and Wang, K. 2018 · 2018
Cited alongside, same era.
Hierarchical gating networks for sequential recommendation
Ma, C.; Kang, P.; and Liu, X. 2019 · 2019
Cited alongside, same era.
Transformers: State-of-the-Art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; Davison, J.; Shleifer, S.; von Platen, P.; Ma, C.; Jernite, Y.; Plu, J.; Xu, C.; Scao, T. L.; Gugger, S.; Drame, M.; Lhoest, Q.; and Rush, A. M. 2020 · 2020
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning
He, J.; Zhou, C.; Ma, X.; Berg-Kirkpatrick, T.; and Neubig, G. 2021 · 2021
Cited alongside, same era.
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
Towards understanding mixture of experts in deep learning
Chen, Z.; Deng, Y.; Wu, Y.; Gu, Q.; and Li, Y. 2022 · 2022
Later among the works it cites.
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
Later among the works it cites.
PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods
Mangrulkar, S.; Gugger, S.; Debut, L.; Belkada, Y.; and Paul, S. 2022 · 2022
Later among the works it cites.
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Wang, J.; Yuan, F.; Cheng, M.; Jose, J. M.; Yu, C.; Kong, B.; He, X.; Wang, Z.; Hu, B.; and Li, Z. 2022 · 2022
Later among the works it cites.
Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
Liao, B.; Tan, S.; and Monz, C. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Whiteningbert: An easy unsupervised sentence embedding approach
Huang, J.; Tang, D.; Zhong, W.; Lu, S.; Shou, L.; Gong, M.; Jiang, D.; and Duan, N. 2021 · 2021
Cited alongside, same era.
HAM: Hybrid Associations Models for Sequential Recommendation
Peng, B.; Ren, Z.; Parthasarathy, S.; and Ning, X. 2021 · 2021
Cited alongside, same era.
Understanding the role of self attention for efficient speech recognition
Shim, K.; Choi, J.; and Sung, W. 2021 · 2021
Cited alongside, same era.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Zaken, E. B.; Ravfogel, S.; and Goldberg, Y. 2021 · 2021
Cited alongside, same era.
Gradient Accumulation: Overcoming Memory Constraints in Deep Learning
Bhattacharyya, M. 2022 · 2022
Cited alongside, same era.
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
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
Closest in time.
Adaptive budget allocation for parameter-efficient fine-tuning
Zhang, Q.; Chen, M.; Bukharin, A.; He, P.; Cheng, Y.; Chen, W.; and Zhao, T. 2023 · 2023
Closest in time.
Sequential recommendation via stochastic self-attention
Fan, Z.; Liu, Z.; Wang, Y.; Wang, A.; Nazari, Z.; Zheng, L.; Peng, H.; and Yu, P. S. 2022 · 2047
Closest in time.