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KDD 2021|美团联合多高校提出多任务学习模型,已应用于联名卡获客场景

2021-08-13 16:36 浏览: 3973 次 我要评论(0 条) 字号:

论文下载:《Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising》

源代码:https://github.com/xidongbo/AITM

招聘信息

美团金融智能应用团队算法岗位持续热招中,诚招优秀算法工程师及专家,坐标北京/上海。招聘岗位包括:

营销算法工程师/专家

  • 服务美团金融各业务场景,负责营销获客、留存促活等场景的算法设计与开发,综合机器学习与优化技术,解决金融营销问题;
  • 沉淀算法平台能力,提升算法应用的效率,提供客群挖掘、权益分配、素材匹配、动态创意、运筹规划、精准触达等智能解决方案;
  • 结合美团金融业务场景,对深度学习、强化学习、知识图谱等人工智能前沿技术探索创新,实施创新技术沉淀和落地。

风控算法工程师/专家

  • 通过机器学习模型与策略的开发优化,持续提升对于金融风险行为的识别能力;
  • 深入理解业务,应用机器学习技术提高风控工作的自动化程度,全面提升业务效率;
  • 跟进人工智能的前沿技术,并在金融风控场景中探索落地。

NLP算法工程师/专家

  • 基于美团金融业务场景,结合自然语言处理和机器学习相关技术,落地智能对话机器人到金融营销、风险管理、客服等多个场景;
  • 参与研发对话机器人的相关项目,包括但不限于语义理解、多轮对话管理等相关算法的开发和优化;
  • 持续跟进学术界和工业界相关技术的发展,并快速应用于项目中。

欢迎感兴趣的同学发送简历至:chenzhen06@meituan.com(邮件标题注明:美团金融智能应用团队)。

参考文献

  • [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


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