Add papers to the model list

[zhujiem]

• [TKDD'2020] Core Interest Network for Click-Through Rate Prediction

• [CIKM2020] Deep Multi-Interest Network for Click-through Rate Prediction

• [CIKM'21] Efficient Learning to Learn a Robust CTR Model for Web-scale Online Sponsored Search Advertising

• [CIKM'21] Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

• [CIKM'21] One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction

• [CIKM'21] AutoIAS: Automatic Integrated Architecture Searcher For Click-Trough Rate Prediction

• [CIKM'21] Click-Through Rate Prediction with Multi-Modal Hypergraphs

• [CIKM'21] AutoHERI: Automated Hierarchical Representation Integration for Post-Click Conversion Rate Estimation

• [CIKM'21] Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

[zhujiem]

• [SIGIR'2021] Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction

• [KDD'2021] Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction

• [KDD'2021] Dual Graph Enhanced Embedding Neural Network for CTR Prediction

• XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction

• Feature Interaction based Neural Network for Click-Through Rate Prediction

• Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction

• A Non-sequential Approach to Deep User Interest Model for CTR Prediction

• AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction

• Feature Interaction based Neural Network for Click-Through Rate Prediction

• Field-Embedded Factorization Machines for Click-through Rate Prediction

• Robust Factorization Machines for User Response Prediction

• Field-wise Learning for Multi-field Categorical Data

• Feature Interaction based Neural Network for Click-Through Rate Prediction

• An Efficient Deep Interaction Network for Click-Through Rate Prediction

• Click-Through Rate Prediction Combining Mutual Information Feature Weighting and Feature Interaction

• GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction

• Empirically Testing Deep and Shallow Ranking Models for Click-Through Rate (CTR) Prediction

• Click-Through Rate Prediction Using Graph Neural Networks and Online Learning

• Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate

• A New Click-Through Rates Prediction Model Based on Deep&Cross Network

• User Response Prediction in Online Advertising

• Evaluating deep learning based models for predicting click through rate

• AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System

• Predicting Response in Mobile Advertising with Hierarchical Importance-Aware Factorization Machine

• Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction

• Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection

• Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

• Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction

• DeepEnFM: Deep neural networks with Encoder enhanced Factorization Machine

• Factorization Machines with Regularization for Sparse Feature Interactions

• Dual-attentional Factorization-Machines based Neural Network for User Response Prediction

• [IJCAI'2020] A Dual Input-aware Factorization Machine for CTR Prediction

• A Dynamic Neural Network Model for Click-Through Rate Prediction in Real-Time Bidding

• [SEKE'20] Deep Graph Attention Neural Network for Click-Through Rate Prediction

• [KDD'20] AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

• [CIKM'19] Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction

• [WWW'20] Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

• [WWW'20] Adversarial Multimodal Representation Learning for Click-Through Rate Prediction

• [SIGIR'2020] AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction

• [IEEE Access'2019] Field-Aware Neural Factorization Machine for Click-Through Rate Prediction

• [] Structured Semantic Model Supported Deep Neural Network for Click-Through Rate Prediction

• [AAAI'19] Interaction-Aware Factorization Machines for Recommender Systems

• Deep Neural Network-Based Click-Through Rate Prediction using Multimodal Features of Online Banners

• [SIGIR'18] Combined Regression and Tripletwise Learning for Conversion Rate Prediction in Real-Time Bidding Advertising

• [SIGIRW'18] Visualizing and Understanding Deep Neural Networks in CTR Prediction

• [KDD'20] Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

[zhujiem]

• [Arxiv'2021] FINT: Field-aware Interaction Neural Network For CTR Prediction
• [SIGIR'2021] Looking at CTR Prediction Again: Is Attention All You Need?