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A configurable, tunable, and reproducible library for CTR prediction

Project description


Click-through rate (CTR) prediction is an important task in many industrial applications such as online advertising, recommender systems, and sponsored search. OpenCTR builds an open-source library for benchmarking existing CTR prediction models.

Model List

CTR prediction models currently available:

Publication Model Paper Available
WWW'07 LR Predicting Clicks: Estimating the Click-Through Rate for New Ads [Microsoft] :heavy_check_mark:
ICDM'10 FM Factorization Machines :heavy_check_mark:
CIKM'15 CCPM A Convolutional Click Prediction Model :heavy_check_mark:
RecSys'16 FFM Field-aware Factorization Machines for CTR Prediction [Criteo] :heavy_check_mark:
RecSys'16 YoutubeDNN Deep Neural Networks for YouTube Recommendations [Google] :heavy_check_mark:
DLRS'16 Wide&Deep Wide & Deep Learning for Recommender Systems [Google] :heavy_check_mark:
ECIR'16 FNN Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction [RayCloud] :heavy_check_mark:
ICDM'16 IPNN Product-based Neural Networks for User Response Prediction :heavy_check_mark:
KDD'16 DeepCross Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features [Microsoft] :heavy_check_mark:
NIPS'16 HOFM Higher-Order Factorization Machines :heavy_check_mark:
IJCAI'17 DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, [Huawei] :heavy_check_mark:
SIGIR'17 NFM Neural Factorization Machines for Sparse Predictive Analytics :heavy_check_mark:
IJCAI'17 AFM Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks :heavy_check_mark:
ADKDD'17 DCN Deep & Cross Network for Ad Click Predictions [Google] :heavy_check_mark:
WWW'18 FwFM Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising [Oath, TouchPal, LinkedIn, Ablibaba] :heavy_check_mark:
KDD'18 xDeepFM xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems [Microsoft] :heavy_check_mark:
KDD'18 DIN Deep Interest Network for Click-Through Rate Prediction [Alibaba] :heavy_check_mark:
CIKM'19 FiGNN FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction :heavy_check_mark:
CIKM'19 AutoInt+ AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks :heavy_check_mark:
RecSys'19 FiBiNET FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction [Sina Weibo] :heavy_check_mark:
WWW'19 FGCNN Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction [Huawei] :heavy_check_mark:
AAAI'19 HFM+ Holographic Factorization Machines for Recommendation :heavy_check_mark:
NeuralNetworks'20 ONN Operation-aware Neural Networks for User Response Prediction :heavy_check_mark:
AAAI'20 AFN+ Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions :heavy_check_mark:
AAAI'20 LorentzFM Learning Feature Interactions with Lorentzian Factorization :heavy_check_mark:
WSDM'20 InterHAt Interpretable Click-through Rate Prediction through Hierarchical Attention :heavy_check_mark:
DLP-KDD'20 FLEN FLEN: Leveraging Field for Scalable CTR Prediction [Tencent] :heavy_check_mark:

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