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RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction., , , , , and . CoRR, (2022)Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer., , , , , , , , and . SIGIR, page 353-362. ACM, (2022)Feature Staleness Aware Incremental Learning for CTR Prediction., , , and . IJCAI, page 2352-2360. ijcai.org, (2023)Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction., , , , and . KDD, page 4661-4672. ACM, (2023)Graph Pre-training and Prompt Learning for Recommendation., , , , and . CoRR, (2023)Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation., , , , , , and . CoRR, (2022)RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction., , , , , and . ACM Trans. Inf. Syst., 41 (3): 69:1-69:26 (2023)Automated Self-Supervised Learning for Recommendation., , , , , and . WWW, page 992-1002. ACM, (2023)Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction., , , , , , , , and . CoRR, (2022)Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation., , , , , , and . WSDM, page 715-723. ACM, (2023)