JointCTR: a joint CTR prediction framework combining feature interaction and sequential behavior learning
Author:
Funder
natural science foundation of shanghai
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-021-02678-8.pdf
Reference38 articles.
1. Guo W, Han J, Tang R, et al. (2019) Order-aware embedding neural network for CTR prediction. In: Proc. of the 42th International ACM SIGIR Conference on Research and Development in Information Retrieval
2. Ktena S I, Myana P K, Tejani A, et al. (2019) Addressing delayed feedback for continuous training with neural networks in CTR prediction. In: Proc. of the 13th ACM Conference on Recommender Systems (RecSys)
3. Bai X, Abasi R, Edizel B, Mantrach A (2019) Position-aware deep character-level CTR prediction for sponsored search. IEEE Trans Knowl Data Eng 33:1722–1736. https://doi.org/10.1109/TKDE.2019.2941881
4. Pan F, Li S, Ao X, et al. (2019) Warm up cold-start advertisements: Improving CTR predictions via learning to learn ID embeddings. In: Proc. of the 42th International ACM SIGIR Conference on Research and Development in Information Retrieval (RecSys)
5. Yang X, Deng T, Tan W, et al. (2019) Learning compositional, visual and relational representations for CTR prediction in sponsored search. In: Proc. of the 28th Conference on Information and Knowledge Management (CIKM)
Cited by 16 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. Context-aware cross feature attentive network for click-through rate predictions;Applied Intelligence;2024-07-13
2. Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction;Applied Sciences;2024-03-26
3. MeFiNet: Modeling multi-semantic convolution-based feature interactions for CTR prediction;Intelligent Data Analysis;2023-11-30
4. Deep Interaction Behavioral Feature Network for Click-Through Rate Prediction;2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI);2023-11-06
5. CFF: combining interactive features and user interest features for click-through rate prediction;The Journal of Supercomputing;2023-09-04
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3