GitHub - Kublai-Jing/TPCF: Implementation of the paper "A Transfer Probabilistic Collective Factorization Model to Handle Sparse Data in Collaborative Filtering", ICDM 2014

Kublai-Jing / TPCF Public

Notifications You must be signed in to change notification settings
Fork 2
Star 2

Implementation of the paper "A Transfer Probabilistic Collective Factorization Model to Handle Sparse Data in Collaborative Filtering", ICDM 2014

2 stars 2 forks Branches Tags Activity

Star

Notifications

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
README.txt		README.txt
code_data.rar		code_data.rar

Repository files navigation

This is the README file for our submission in ICDM 2014: 


We provide MATALB sources and dataset we used in the experiments.



Purpose:
=========================================================================================================================================================

The current version of the code is purely for the reason to reproduce the results reported in the paper with TPCF.

There is absolutely NO other guarantees. 

A.k.a. No comments on code. No documentation. No optimization for speed and readability. 

We will keep refining the code for later research usage.

Dataset:
=========================================================================================================================================================
The package contains datasets for the "Netflix-MovieLens" task.


Usage:
=========================================================================================================================================================

Simply run the script "run_10d", this will store RMSE

The exact numbers may be slightly different from those reported in the paper due to random number generator.

Our MATLAB version is 2012b, I am not sure whether there will be any technical problem with older/newer versions.