Code for 'Collaborative Deep Learning for Recommender Systems' - SIGKDD
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README.md

This is the code for CDL (collaborative deep learning). It consists of two parts: a matlab component and a C++ component.

To run this code you need to make sure:

  1. you have the mult_nor.mat file located in cdl-release/example (can be downloaded from www.wanghao.in/code/cdl-release.rar)
  2. you have matlab with GPU support
  3. you have installed the GSL library (see www.gnu.org/software/gsl/)

After installing GSL, please remember to add the path of the dynamic library (the directory with files like libgsl.so.0.10.0) to LD_LIBRARY_PATH in your .bashrc. Or you can directly change the code in cdl.m around Line 586 where LD_LIBRARY_PATH is exported.

To save the pain of handling memory and variables in mex, we directly compiled a C++ program for the updates of U and V and call the program from matlab. If your program runs without trouble, congratulations! If not, you may have to re-compiled the C++ component which is in the folder 'ctr-part'. You will need to install the GSL before doing that.

To quickly run the program you can directly call the cdl_main.m.

To quickly know what CDL is doing click on collaborative-dl.ipynb (demo in this notenook uses the MXNet-version code, not this matlab/C++ version).

MXNet version for simplified CDL: https://github.com/js05212/MXNet-for-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):

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}
}