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R functions for fitting latent factor models with internal computation in C/C++

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Merge pull request #6 from beechung/master

complete ARS logistic response model pull
latest commit cd30125f54
beechung authored February 29, 2012
Octocat-spinner-32 doc update tutorial.pdf January 10, 2012
Octocat-spinner-32 src remove wrong files February 29, 2012
Octocat-spinner-32 test-data revise the tutorial & add predict.bst January 09, 2012
Octocat-spinner-32 todo Add more readable error message & add a simulated dataset January 02, 2012
Octocat-spinner-32 .gitignore add a initial draft of the tutorial for the multicontext code January 03, 2012
Octocat-spinner-32 LICENSE chech in the code October 19, 2011
Octocat-spinner-32 Makefile update December 20, 2011
Octocat-spinner-32 Makevars chech in the code October 19, 2011
Octocat-spinner-32 README Add LDA-RLFM (i.e., fLDA) code into this project. January 18, 2012
README
########################################################
     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
   
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