欢迎您访问 最编程 本站为您分享编程语言代码,编程技术文章!
您现在的位置是: 首页

KDD 2021|美团联合多所大学提出多任务学习模型,应用于联名卡客户获取场景

最编程 2024-05-03 17:35:33
...
  • [1] Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In KDD. 1930–1939.
  • [2] Zhen Qin, Yicheng Cheng, Zhe Zhao, Zhe Chen, Donald Metzler, and Jingzheng Qin. 2020. Multitask Mixture of Sequential Experts for User Activity Streams. In KDD. 3083–3091.
  • [3] Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. In RecSys. 269–278.
  • [4] Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John E Hopcroft. 2016. Convergent Learning: Do different neural networks learn the same representations? In ICLR.
  • [5] Matthew D Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In ECCV. 818–833.
  • [6] Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. 2014. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014).
  • [7] Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, and Depeng Jin. 2019. Neural multi-task recommendation from multi-behavior data. In ICDE. 1554–1557.
  • [8] Chen Gao, Xiangnan He, Danhua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang Song, and Depeng Jin. 2019. Learning to Recommend with Multiple Cascading Behaviors. TKDE (2019).
  • [9] Xiao Ma, Liqin Zhao, Guan Huang, ZhiWang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire space multi-task model: An effective approach for estimating post-click conversion rate. In SIGIR. 1137–1140.
  • [10] Hong Wen, Jing Zhang, Yuan Wang, Fuyu Lv, Wentian Bao, Quan Lin, and Keping Yang. 2020. Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction. In SIGIR. 2377–2386.
  • [11] https://tianchi.aliyun.com/datalab/dataSet.html?dataId=408