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Research Code for Fitting Latent Factor Models
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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