Spatio-temporal models for estimating click-through rate
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ACM Press
Cited by 61 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21
2. Workshop on Learning and Evaluating Recommendations with Impressions (LERI);Proceedings of the 17th ACM Conference on Recommender Systems;2023-09-14
3. Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences;Proceedings of the 31st ACM International Conference on Information & Knowledge Management;2022-10-17
4. Click-through rate prediction in online advertising: A literature review;Information Processing & Management;2022-03
5. Traffic Anomaly Prediction Based on Joint Static-Dynamic Spatio-Temporal Evolutionary Learning;IEEE Transactions on Knowledge and Data Engineering;2022
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