| 1 |
WWW'07 |
LR |
Predicting Clicks: Estimating the Click-Through Rate for New Ads 🚩Microsoft |
↗️ |
| 2 |
ICDM'10 |
FM |
Factorization Machines |
↗️ |
| 3 |
CIKM'13 |
DSSM |
Learning Deep Structured Semantic Models for Web Search using Clickthrough Data 🚩Microsoft |
↗️ |
| 4 |
CIKM'15 |
CCPM |
A Convolutional Click Prediction Model |
↗️ |
| 5 |
RecSys'16 |
FFM |
Field-aware Factorization Machines for CTR Prediction 🚩Criteo |
↗️ |
| 6 |
RecSys'16 |
YoutubeDNN |
Deep Neural Networks for YouTube Recommendations 🚩Google |
↗️ |
| 7 |
DLRS'16 |
Wide&Deep |
Wide & Deep Learning for Recommender Systems 🚩Google |
↗️ |
| 8 |
ICDM'16 |
IPNN |
Product-based Neural Networks for User Response Prediction |
↗️ |
| 9 |
KDD'16 |
DeepCross |
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features 🚩Microsoft |
↗️ |
| 10 |
NIPS'16 |
HOFM |
Higher-Order Factorization Machines |
↗️ |
| 11 |
IJCAI'17 |
DeepFM |
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction 🚩Huawei |
↗️ |
| 12 |
SIGIR'17 |
NFM |
Neural Factorization Machines for Sparse Predictive Analytics |
↗️ |
| 13 |
IJCAI'17 |
AFM |
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks |
↗️ |
| 14 |
ADKDD'17 |
DCN |
Deep & Cross Network for Ad Click Predictions 🚩Google |
↗️ |
| 15 |
WWW'18 |
FwFM |
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising 🚩Oath, TouchPal, LinkedIn, Alibaba |
↗️ |
| 16 |
KDD'18 |
xDeepFM |
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems 🚩Microsoft |
↗️ |
| 17 |
KDD'18 |
DIN |
Deep Interest Network for Click-Through Rate Prediction 🚩Alibaba |
|
| 18 |
CIKM'19 |
FiGNN |
FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction |
↗️ |
| 19 |
CIKM'19 |
AutoInt/AutoInt+ |
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks |
↗️ |
| 20 |
RecSys'19 |
FiBiNET |
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction 🚩Sina Weibo |
↗️ |
| 21 |
WWW'19 |
FGCNN |
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction 🚩Huawei |
↗️ |
| 22 |
AAAI'19 |
HFM/HFM+ |
Holographic Factorization Machines for Recommendation |
↗️ |
| 23 |
Arxiv'19 |
DLRM |
Deep Learning Recommendation Model for Personalization and Recommendation Systems 🚩Facebook |
↗️ |
| 24 |
NeuralNetworks'20 |
ONN |
Operation-aware Neural Networks for User Response Prediction |
↗️ |
| 25 |
AAAI'20 |
AFN/AFN+ |
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions |
↗️ |
| 26 |
AAAI'20 |
LorentzFM |
Learning Feature Interactions with Lorentzian Factorization 🚩eBay |
↗️ |
| 27 |
WSDM'20 |
InterHAt |
Interpretable Click-through Rate Prediction through Hierarchical Attention 🚩NEC Labs, Google |
↗️ |
| 28 |
DLP-KDD'20 |
FLEN |
FLEN: Leveraging Field for Scalable CTR Prediction 🚩Tencent |
↗️ |
| 29 |
CIKM'20 |
DeepIM |
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions 🚩Alibaba, RealAI |
↗️ |
| 30 |
WWW'21 |
FmFM |
FM^2: Field-matrixed Factorization Machines for Recommender Systems 🚩Yahoo |
↗️ |
| 31 |
WWW'21 |
DCN-V2 |
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems 🚩Google |
↗️ |
| 32 |
CIKM'21 |
DESTINE |
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction 🚩Alibaba |
↗️ |
| 33 |
CIKM'21 |
EDCN |
Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models 🚩Huawei |
↗️ |
| 34 |
DLP-KDD'21 |
MaskNet |
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask 🚩Sina Weibo |
↗️ |
| 35 |
SIGIR'21 |
SAM |
Looking at CTR Prediction Again: Is Attention All You Need? 🚩BOSS Zhipin |
↗️ |
| 36 |
KDD'21 |
AOANet |
Architecture and Operation Adaptive Network for Online Recommendations 🚩Didi Chuxing |
↗️ |