Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
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