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Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1930–1939
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018b · 1939
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Transfer learning
Lisa Torrey and Jude Shavlik. 2010 · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
A unified perspective on multi-domain and multi-task learning
Yongxin Yang and Timothy M Hospedales. 2014 · 2014
Earlier work this paper cites.
Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3994–4003
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016 · 2016
Earlier work this paper cites.
Sluice networks: Learning what to share between loosely related tasks
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2017 · 2017
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
Cited alongside, same era.
Deep mixture of experts via shallow embedding
Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, and Joseph E Gonzalez. 2018 · 2018
Cited alongside, same era.
Snr: Sub-network routing for flexible parameter sharing in multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 216–223
Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, and Ed H Chi. 2019 · 2019
Cited alongside, same era.
Multiple relational attention network for multi-task learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1123–1131
Jiejie Zhao, Bowen Du, Leilei Sun, Fuzhen Zhuang, Weifeng Lv, and Hui Xiong. 2019 · 2019
Cited alongside, same era.
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts
Wouter Kool, Chris J Maddison, and Andriy Mnih. 2021 · 2021
Later among the works it cites.
Credit Risk and Limits Forecasting in E-Commerce Consumer Lending Service via Multi-view-aware Mixture-of-experts Nets. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 229–237
Ting Liang, Guanxiong Zeng, Qiwei Zhong, Jianfeng Chi, Jinghua Feng, Xiang Ao, and Jiayu Tang. 2021 · 2021
Later among the works it cites.
Dense-to-Sparse Gate for Mixture-of-Experts
Xiaonan Nie, Shijie Cao, Xupeng Miao, Lingxiao Ma, Jilong Xue, Youshan Miao, Zichao Yang, Zhi Yang, and Bin Cui. 2021 · 2021
Later among the works it cites.
Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 1038–1046
Xichuan Niu, Bofang Li, Chenliang Li, Jun Tan, Rong Xiao, and Hongbo Deng. 2021 · 2021
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Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce. In Proceedings of the International Conference on Data Mining Workshops (ICDMW) . IEEE, 127–135
Yuting Chen, Yanshi Wang, Yabo Ni, An-Xiang Zeng, and Lanfen Lin. 2020 · 2020
Cited alongside, same era.
Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2605–2612
Pengcheng Li, Runze Li, Qing Da, An-Xiang Zeng, and Lijun Zhang. 2020 · 2020
Cited alongside, same era.
Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Proceedings of 14th ACM Conference on Recommender Systems . 269–278
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020 · 2020
Cited alongside, same era.
Recommendation for new users and new items via randomized training and mixture-of-experts transformation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 1121–1130
Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah, and James Caverlee. 2020 · 2020
Cited alongside, same era.
Self-Supervised Learning on Users’ Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 3828–3837
Yulong Gu, Wentian Bao, Dan Ou, Xiang Li, Baoliang Cui, Biyu Ma, Haikuan Huang, Qingwen Liu, and Xiaoyi Zeng. 2021 · 2021
Cited alongside, same era.
Entire space multi-task model: An effective approach for estimating post-click conversion rate. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 1137–1140
Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018a
Cited in the paper.
Later among the works it cites.
Scaling Vision with Sparse Mixture of Experts
Carlos Riquelme Ruiz, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby. 2021 · 2021
Later among the works it cites.
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4104–4113
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, et al · 2021
Later among the works it cites.
Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, and Yu Chen. 2021 · 2021
Later among the works it cites.
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint. In Proceedings of the IEEE Conference on Data Engineering (ICDE) . IEEE, 2453–2463
Zhuojian Xiao, Yunjiang Jiang, Guoyu Tang, Lin Liu, Sulong Xu, Yun Xiao, and Weipeng Yan. 2021 · 2021
Later among the works it cites.
Exploring Sparse Expert Models and Beyond
An Yang, Junyang Lin, Rui Men, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Jiamang Wang, Yong Li, et al · 2021
Later among the works it cites.