Kung-hsiang (Steeve), Huang (Rosetta.ai); Yi-fu, Fu; Yi-ting, Lee; Zong-han, Lee; Yao-chun, Jan (National Taiwan University); Yi-hui, Lee (University of Texas at Dallas)
Contact: steeve@rosetta.ai
This repository contains RosettaAI's approach to the 2019 ACM Recys Challenge. Instead of treating it as a ranking problem, we use Binary Cross Entropy as our loss function. Three different models were implemented:
- Neural Networks (based on DeepFM)
- LightGBM
- XGBoost
- Ubuntu 16.04
- CUDA 9.0
- Python==3.6.8
- Numpy==1.16
- Pandas==0.24.2
- PyTorch==1.1.0
- Sklearn==0.21.2
- Scipy==1.3.0
- LightGBM==2.2.4
- XGBoost==0.9
- timezonefinder==4.0.3
- geopy==1.20.0