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Learning to Retrieve User Behaviors for Click-through Rate Estimation., , , , , , , , , and . ACM Trans. Inf. Syst., 41 (4): 98:1-98:31 (October 2023)MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction., , , , , , and . KDD, page 1384-1395. ACM, (2023)On-device Integrated Re-ranking with Heterogeneous Behavior Modeling., , , , , , , and . KDD, page 5225-5236. ACM, (2023)ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop., , , , , and . KDD, page 5728-5738. ACM, (2023)Deep Learning for Click-Through Rate Estimation., , , , and . IJCAI, page 4695-4703. ijcai.org, (2021)PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations., , , , , , , , and . SIGIR, page 1498-1507. ACM, (2023)User Behavior Enriched Temporal Knowledge Graphs for Sequential Recommendation., , , , , , and . WSDM, page 266-275. ACM, (2024)LightCS: Selecting Quadratic Feature Crosses in Linear Complexity., , , , , , and . WWW (Companion Volume), page 38-46. ACM, (2024)A Comprehensive Survey on Automated Machine Learning for Recommendations., , , , , and . Trans. Recomm. Syst., 2 (2): 13:1-13:38 (June 2024)Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation., , , , , , and . WSDM, page 779-787. ACM, (2024)