reczoo/BARS

Add papers to the model list

Closed this issue · 2 comments

zhujiem commented

  • [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 commented

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