KDD 2021|美團聯合多高校提出多任務學習模型,已應用於聯名卡獲客場景

美團技術團隊 2021-08-15 08:22:15 阅读数:204

本文一共[544]字,预计阅读时长:1分钟~
kdd 多高 高校 提出 模型

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

源代碼:https://github.com/xidongbo/AITM

招聘信息

美團金融智能應用團隊算法崗比特持續熱招中,誠招優秀算法工程師及專家,坐標北京/上海。招聘崗比特包括:

營銷算法工程師/專家

  • 服務美團金融各業務場景,負責營銷獲客、留存促活等場景的算法設計與開發,綜合機器學習與優化技術,解决金融營銷問題;
  • 沉澱算法平臺能力,提昇算法應用的效率,提供客群挖掘、權益分配、素材匹配、動態創意、運籌規劃、精准觸達等智能解决方案;
  • 結合美團金融業務場景,對深度學習、强化學習、知識圖譜等人工智能前沿技術探索創新,實施創新技術沉澱和落地。

風控算法工程師/專家

  • 通過機器學習模型與策略的開發優化,持續提昇對於金融風險行為的識別能力;
  • 深入理解業務,應用機器學習技術提高風控工作的自動化程度,全面提昇業務效率;
  • 跟進人工智能的前沿技術,並在金融風控場景中探索落地。

NLP算法工程師/專家

  • 基於美團金融業務場景,結合自然語言處理和機器學習相關技術,落地智能對話機器人到金融營銷、風險管理、客服等多個場景;
  • 參與研發對話機器人的相關項目,包括但不限於語義理解、多輪對話管理等相關算法的開發和優化;
  • 持續跟進學術界和工業界相關技術的發展,並快速應用於項目中。

歡迎感興趣的同學發送簡曆至:[email protected](郵件標題注明:美團金融智能應用團隊)。

參考文獻

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  • [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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