├── .gitignore ├── Data └── features │ └── predict_feature.csv ├── Paper ├── Ad Click Prediction a View from the Trenches.pdf ├── Click-through Prediction for Advertising in Twitter Timeline.pdf ├── Deep CTR Prediction in Display Advertising.pdf ├── Deep Crossing- Web-Scale Modeling without Manually Crafted Combinatorial Features.pdf ├── Factorization Machines with libFM.pdf ├── Factorization Machines--Steffen Rendle.pdf ├── Field-aware Factorization Machines for CTR Prediction.pdf ├── Field-aware Factorization Machines in a Real-world Online Advertising System-ind0438-juanA.pdf ├── Recurrent Neural Networks with Top-k Gains for Session-based Recommendations.pdf ├── SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS.pdf ├── Wide & Deep Learning for Recommender Systems.pdf ├── XGBoost A Scalable Tree Boosting System.pdf ├── predicting-clicks-facebook.pdf ├── 【ECIR-16-FNN】Deep Learning over Multi-field Categorical Data--A Case Study on User Response Prediction.pdf ├── 【IJCAI-17】-DeepFM-A Factorization-Machine based Neural Network for CTR Prediction.pdf ├── 【NIPS-2017】lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf └── 【SIGIR-17】Neural Factorization Machines for r Sparse Predictive Analytics.pdf ├── Python ├── __pycache__ │ └── gbdt_lr_train.cpython-36.pyc ├── based.py ├── baseline.py ├── baseline_wh.ipynb ├── features.py ├── gbdt_lr_train.py └── statistics.py ├── README.md ├── Stat_output ├── context_id.csv ├── context_page_id.csv ├── context_timestamp.csv ├── instance_id.csv ├── isTrain.csv ├── is_trade.csv ├── item_brand_id.csv ├── item_category_list.csv ├── item_city_id.csv ├── item_collected_level.csv ├── item_id.csv ├── item_price_level.csv ├── item_property_list.csv ├── item_pv_level.csv ├── item_sales_level.csv ├── predict_category_property.csv ├── shop_id.csv ├── shop_review_num_level.csv ├── shop_review_positive_rate.csv ├── shop_score_delivery.csv ├── shop_score_description.csv ├── shop_score_service.csv ├── shop_star_level.csv ├── test_time.csv ├── train_time.csv ├── train_time.xlsx ├── user_age_level.csv ├── user_gender_id.csv ├── user_id.csv ├── user_occupation_id.csv └── user_star_level.csv └── data ├── sample.txt └── test_sample.txt /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SayaZhang/IJCAI-2018-Ctr/HEAD/.gitignore -------------------------------------------------------------------------------- /Data/features/predict_feature.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SayaZhang/IJCAI-2018-Ctr/HEAD/Data/features/predict_feature.csv -------------------------------------------------------------------------------- /Paper/Ad Click Prediction a View from the Trenches.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SayaZhang/IJCAI-2018-Ctr/HEAD/Paper/Ad Click 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