Awesome
This is the official code for CDL (collaborative deep learning). More details on models are results can be found in this blog post. It consists of two parts: a matlab component and a C++ component.
To run this code you need to make sure:
- you have the mult_nor.mat file located in cdl-release/example (can be downloaded from www.wanghao.in/code/cdl-release.rar)
- you have matlab with GPU support
- 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):
Keras code by zoujun123.
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}
}