js05212 / MXNet-for-CDL

Official MXNet code for 'Collaborative Deep Learning for Recommender Systems' - SIGKDD

This is the MXNet version of CDL used in the KDD 2016 hands-on tutorial for MXNet. Note that the code is a simplified version of CDL and is used for demonstration only (you can also find the corresponding IPython notebook). We do not use pretrain and sigmoid activation (as used in the matlab/C++ code for CDL) in this version of code, which may harm the performance.

To run the code, please type in (after installing MXNet): python cdl.py in the command line.

More details on the work and some direct extensions can be found at http://wanghao.in/CDL.htm.

Matlab version for full CDL: https://github.com/js05212/CDL.

Data: https://www.wanghao.in/data/ctrsr_datasets.rar.

Slides: http://wanghao.in/slides/CDL_slides.pdf and http://wanghao.in/slides/CDL_slides_long.pdf.

Other implementations (third-party):

PyTorch code by Zach Langley.

Tensorflow code by gtshs2.

Keras code by zoujun123.

Python code by xiaoouzhang.

Reference:

Collaborative Deep Learning for Recommender Systems

@inproceedings{DBLP:conf/kdd/WangWY15,
  author    = {Hao Wang and
               Naiyan Wang and
               Dit{-}Yan Yeung},
  title     = {Collaborative Deep Learning for Recommender Systems},
  booktitle = {SIGKDD},
  pages     = {1235--1244},
  year      = {2015}
}