########################################################
     Research Code for Fitting Latent Factor Models
########################################################

Authors: Bee-Chung Chen, Deepak Agarwal and Liang Zhang
         Yahoo! Labs


I. Introduction

   This code base consists of algorithms for fitting factor models written in
   R and C/C++.  The entry point of any fitting algorithm is in R.  The
   computationally intensive parts are written in C/C++. The models and 
   algorithms have been described in the following papers.
   
   [1] Bee-Chung Chen, Jian Guo, Belle Tseng, Jie Yang. User reputation in a
       comment rating environment. KDD 2011.
   [2] Deepak Agarwal, Bee-Chung Chen. Regression-based latent factor models.
       KDD 2009.
   [3] Deepak Agarwal, Bee-Chung Chen, Bo Long. Localized factor models for 
       multi-context recommendation. KDD 2011.
   [4] Deepak Agarwal, Bee-Chung Chen. Latent OLAP: Data cubes over latent 
       variables. SIGMOD Conference 2011.
   [5] Deepak Agarwal, Bee-Chung Chen. fLDA: Matrix factorization through 
       latent Dirichlet allocation. WSDM 2010.
       
II. Tutorial

   See doc/tutorial.pdf for a tutorial on how to use this package to fit
   the latent factor models described in [1,2].

III. Compilation

   You need to have R installed before compiling the code.
   To install R, see: http://www.r-project.org/
   You have to install R from source on a linux machine.
   It is recommended to use R version >= 2.10.1.
   
   The following R packages also need to be installed.
      Matrix
      glmnet
   
   To compile the C/C++ code, just type make.

IV. Examples

   Localized factor model (multi-context, multi-application factorization) [2]:
        src/multi-app/R/example/fitting.R

   fLDA model (LDA topic modeling + Matrix factorization) [5]:
        src/LDA-RLFM/R/model/examples.R