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

, , , , and . KDD, page 2671-2679. ACM, (2019)

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Practice on Effectively Extracting NLP Features for Click-Through Rate Prediction., , , and . CIKM, page 4887-4893. ACM, (2023)Deep Interest Evolution Network for Click-Through Rate Prediction., , , , , , , and . AAAI, page 5941-5948. AAAI Press, (2019)Rocket Launching: A Universal and Efficient Framework for Training Well-Performing Light Net., , , , , and . AAAI, page 4580-4587. AAAI Press, (2018)Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction., , , , , , , and . CoRR, (2021)Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling., , , , , and . CoRR, (2019)A Compare-Aggregate Model with Dynamic-Clip Attention for Answer Selection., , , , and . CIKM, page 1987-1990. ACM, (2017)CAN: Feature Co-Action Network for Click-Through Rate Prediction., , , , , , , , , and 5 other author(s). WSDM, page 57-65. ACM, (2022)Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction., , , , , , , , , and 1 other author(s). SIGIR, page 565-574. ACM, (2019)KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging., , , , , , and . CIKM, page 3684-3693. ACM, (2022)Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction., , , , , , , and . KDD, page 2050-2058. ACM, (2022)