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In recent years, thanks to the rapid development of deep learning (DL), DL-based multi-task learning (MTL) has made significant progress, and it has been successfully applied to recommendation systems (RS).
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Ma, J.; Zhao, Z.; Yi, X.; Chen, J.; Hong, L.; and Chi, E. H. 2018a · 1939
Earlier work this paper cites.
Visualizing High-Dimensional Data Using t-SNE
Hinton, G. E. 2008 · 2008
Earlier work this paper cites.
Factorized orthogonal latent spaces
Salzmann, M.; Ek, C. H.; Urtasun, R.; and Darrell, T. 2010 · 2010
Earlier work this paper cites.
Domain separation networks
Bousmalis, K.; Trigeorgis, G.; Silberman, N.; Krishnan, D.; and Erhan, D. 2016 · 2016
Cited alongside, same era.
Generative adversarial multi-task learning for face sketch synthesis and recognition
Wan, W.; and Lee, H. J. 2019 · 2019
Cited alongside, same era.
Multi-level deep cascade trees for conversion rate prediction in recommendation system
Wen, H.; Zhang, J.; Lin, Q.; Yang, K.; and Huang, P. 2019 · 2019
Cited alongside, same era.
Entire space multi-task model: An effective approach for estimating post-click conversion rate
Ma, X.; Zhao, L.; Huang, G.; Wang, Z.; Hu, Z.; Zhu, X.; and Gai, K. 2018b
Cited in the paper.
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Tang, H.; Liu, J.; Zhao, M.; and Gong, X. 2020 · 2020
Later among the works it cites.
MSSM: A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning
Ding, K.; Dong, X.; He, Y.; Cheng, L.; Fu, C.; Huan, Z.; Li, H.; Yan, T.; Zhang, L.; and Zhang, X. a. 2021 · 2021
Later among the works it cites.
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