A tensorflow implementation of DESA model. This is the experiment code for the KDD'18 submitted paper "Deep Survival Analysis for Fine-grained Bid Landscape Forecasting in Real-time Bidding Advertising". If you have any questions, please feel free to contact Kan Ren (kren@apex.sjtu.edu.cn), Jiarui Qin (qinjr@apex.sjtu.edu.cn) and Lei Zheng(zhenglei@apex.sjtu.edu.cn).

Our model is DESA model. The baseline models are COX-NN, MM, STM, MTLSA, RNN and DNN. Among the baseline implementations, we forked the code of STM(it also has the implementation of MM) and MTLSA. Of course, we also made some midifications on the two projects to fit them for our experiments, To get the modified code, you can click MTLSA @ 712e3bc and STM_MM @ 2d57f03 to get them. Many thanks to the authors of STM/MM and MTLSA. Other baselines' implementations are in python directory.

We have uploaded a tiny data sample for training and evaluation. The full dataset for this project can be download from this link. After download please replace the sample data in data/ folder with the full data files.

TensorFlow(>=1.3) and dependant packages (e.g., numpy, sklearn and matplotlib) should be pre-installed before running the code.

After package installation, you can simple run the code in python directory with the demo tiny dataset(sampled from campaign 2259). The outputs of the code are in `python/output' directory.

python3 coxnn.py [campaign] [learn_rate] [batch_size] [hidden_layer_size] [threshold] [w_k] [w_lambda] #for COX-NN
python RNN.py 0.0001      # for RNN
python DNN.py 0.0001      # for DNN
python DESA.py 0.0001     # for DESA

We have set default hyperparameter in the model implementation. So the parameter arguments are optional for running the code.

The result will be printed on the screen with the format: Camp, Train/Test, Step, Cross Entropy, AUC, ANLP, Total Loss, batch size, hidden state size, learing rate, anlp learning rate, alpha, beta.