-
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications --4Paradigm, 2019, link
-
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction --Huawei, 2020, link
-
AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction --Huawei, CIKM2020, link
-
AutoHERI: Automated Hierarchical Representation Integration for Post-Click Conversion Rate Estimation --Alibaba, 2021, link
-
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction --FB, 2020, link
-
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data --Alibaba, 2021, link
-
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks --Alibaba, 2020, link
-
Mining Cross Features for Financial Credit Risk Assessment --CAS, CIKM2021, link
-
AutoIAS: Automatic Integrated Architecture Searcher for Click-Trough Rate Prediction --Tsinghua, CIKM2021, link
-
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks --Peking, 2018, link
- A Meta-Learning Perspective on Cold-Start Recommendations for Items --Twitter, 2017, link
- Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks -- WeChat, 2021, link
- Content-aware Neural Hashing for Cold-start Recommendation, SIGIR 2020, link, code
- Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction. --Alibaba, SIGIR2021, link, code
- Privileged Graph Distillation for Cold Start Recommendation --SIGIR 2021, link
- Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings --中科院, SIGIR2019, link
