CTR点击率预估论文集锦

CTR预估对于搜索、推荐和广告都是非常重要的一个场景，近年来CTR预估技术更新迭代，层出不穷。这篇文章将记录CTR预估著名模型的相关论文。以下按照年份整理。

(DFN). Ruobing Xie. Deep Feedback Network for Recommendation，2020，IJCAI（CCF-A）. 出自腾讯微信团队.
(DMR). Zequn Lyu. Deep Match to Rank Model for Personalized Click-Through Rate Prediction, AAAI (CCF-A), 出自阿里团队。
(DTS). Shu-Ting Shi. Deep Time-Stream Framework for Click-through Rate Prediction by Tracking Interest Evolution, AAAI (CCF-A), 出自南京大学和阿里合作。
(UBR4CTR). Jiarui Qin.User Behavior Retrieval for Click-Through Rate Prediction, SIGIR (CCF-A), 出自上海交通大学。
(InterHAt). Zeyu Li. Interpretable Click-Through Rate Prediction through Hierarchical Attention, WSDM (CCF-B), 出自加利福尼亚大学。
(MiNet). Wentao Ouyang. MiNet: MixedInterest Network for Cross-Domain Click-Through Rate Prediction, CIKM (CCF-B), 出自阿里智能营销平台。

(DNN双塔). Xinyang Yi. Sampling-bias-corrected neural modeling for large corpus item recommendations, RecSys. 出自谷歌YouTube团队。
(FAT-DeepFFM). Junlin Zhang. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine, ICDM (CCF-B), 出自新浪团队。
(FGCNN). Bin Liu. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction, WWW (CCF-A)，出自华为团队。
(DSTN). Wentao Ouyang. Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction, KDD (CCF-A), 出自阿里智能营销平台团队。
(MA-DNN). Wentao Ouyang. Click-Through Rate Prediction with the User Memory Network, KDD-workshop, 出自阿里智能营销平台团队。
(DeepMCP). Wentao Ouyang. Representation Learning-Assisted Click-Through Rate Prediction, IJCAI, 出自阿里智能营销平台团队。

(xDeepFM). Jianxun Lian. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, KDD (CCF-A)，出自微软和中国科学技术大学合作。
(DIEN). Guorui Zho. Deep Interest Evolution Network for Click-Through Rate Prediction, AAAI (CCF-A), 出自阿里团队。
(DIN). Guorui Zhou. Deep Interest Network for Click-Through Rate Prediction, KDD (CCF-A)，出自阿里团队。
(Airbnb-Embedding). Mihajlo Grbovic. Real-time Personalization using Embeddings for Search Ranking at Airbnb, KDD (CCF-A)，出自Airbnb团队。
(DRN). Guanjie Zheng. DRN: A Deep Reinforcement Learning Framework for News Recommendation, WWW (CCF-A)，出自微软和宾夕法尼亚州立大学合作。
(ESSM). Xiao Ma. Entire Space Multi-Task Model An Effective Approach for Estimating Post-Click Conversion Rate, SIGIR (CCF-A), 出自阿里团队。
(FwFM). Junwei Pan. Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising, WWW (CCF-A)，出自雅虎研究院。

(DeepFM). Huifeng Guo. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, IJCAI (CCF-A)，出自华为和哈尔滨工业大学合作。
(AFM). Jun Xiao. Attentional factorization machines learning the weight of feature interactions via attention networksIJCAI (CCF-A)，出自浙江大学。
(NCF). Xiangnan He. Neural Factorization Machines for Sparse Predictive Analytics, SIGIR (CCF-A), 出自中国科学技术大学。

(FNN). Weinan Zhang. Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction, ECIR (CCF-C), 出自伦敦大学。
(PNN). Yanru Qu. Product-based Neural Networks for User Response Prediction, ICDM (CCF-B), 出自上海交通大学。
(Wide&Deep). Heng-Tze Cheng. Wide & Deep Learning for Recommender Systems, RecSys, 出自谷歌团队。
(Item2Vec). Oren Barkan. ITEM2VEC: Neural item embedding for collaborative filtering, RecSys, 出自微软团队。
(FFM). Yu-Chin Juan. Field-aware Factorization Machines for CTR Prediction, RecSys, 出自Criteo团队。
(HOFMs). Mathieu Blondel. Higher-Order Factorization Machines, NIPS (CCF-A)，出自NTT和北海道大学合作。

（GDBT LR）. Practical Lessons from Predicting Clicks on Ads at Facebook, KDD WorkShop, 出自Facebook团队。2014年。
（DSSM）. Po-Sen Huang. Learning deep structured semantic models for web search using clickthrough data, CIKM (CCF-B), 出自伊利诺伊大学厄巴纳-香槟分校和微软合作。2013年。
（FM）. Steffen Rendle. Factorization Machines, ICDM (CCF-B), 出自大阪大学。2010年。

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