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Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
時間 2021-01-13
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本文主要記錄了這篇文章的主要方法和貢獻,以及個人的一些思考和想法,歡迎討論! 最開始是在語雀寫的,導出後可能格式有點問題,歡迎移步語雀: https://www.yuque.com/docs/share/fee366de-68ba-4254-9a57-cccfe3edd356?# Alibaba, KDD 2019 https://arxiv.org/abs/1905.09248 Cont
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相關文章
1.
Paper Notes: Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommenda
2.
ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation 詳解
3.
What’s a Good Clickthrough Rate? New Benchmark Data for Google AdWords
4.
《User Modeling with Neural Network for Review Rating Prediction》評論打分預測
5.
#Paper Reading# Deep Interest Network for Click-Through Rate Prediction
6.
Deep Interest Network for Click-Through Rate Prediction
7.
論文Disguise Adversarial Networks for Click-through Rate Prediction
8.
IJCAI 2018 Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic
9.
Controllable Multi-Interest Framework for Recommendation
10.
『 DSSM』A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
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