LightCTR

LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models. It provides several off-the-shelf popular CTR models for you to use. And it also contains some useful model blocks to help you build your own model quickly.

Have a quick start with the example script ./examples/ctr_predict.py

A small example dataset avazu_1w.txt is provided in ./datasets, It is sampled from a kaggle dataset with 10000 rows.

Models List

| Model | Paper | | :------------------------------------------: | :---------------- | | Factorization-supported Neural Network (FNN) | [ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction| | Product-based Neural Network (PNN) | [ICDM 2016]Product-based neural networks for user response prediction| | Wide & Deep | [DLRS 2016]Wide & Deep Learning for Recommender Systems| | DeepFM | [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction| | Deep & Cross Network (DCN) | [ADKDD 2017]Deep & Cross Network for Ad Click Predictions | | xDeepFM | [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | | Neural Factorization Machine (NFM) | [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics | | Attentional Factorization Machine (AFM) | [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks | | AutoInt | [arxiv 2018]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | | Convolutional Click Prediction Model (CCPM) | [CIKM 2015]A Convolutional Click Prediction Model | | Feature Generation by Convolutional Neural Network (FGCNN) | [WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction | | Mixed Logistic Regression (MLR) | [arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction | | FiBiNET | [RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction | | NFFM | [arxiv 2019]Operation-aware Neural Networks for User Response Prediction |