######################################################## 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