Recosystem is an R wrapper of the LIBMF library developed by Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin and Chih-Jen Lin (http://www.csie.ntu.edu.tw/~cjlin/libmf/), an open source library for recommender system using marix factorization.

A more detailed introduction can be found in the vignette of this package.

The main task of recommender system is to predict unknown entries in the rating matrix based on observed values, as is shown in the table below:

Each cell with number in it is the rating given by some user on a specific item, while those marked with question marks are unknown ratings that need to be predicted. In some other literatures, this problem may be given other names, e.g. collaborative filtering, matrix completion, matrix recovery, etc.

LIBMF itself is a parallelized library, meaning that users can take advantage of multicore CPUs to speed up the computation. It also utilizes some advanced CPU features to further improve the performance.

Recosystem is a complete wrapper of LIBMF, hence the features of LIBMF are all included in Recosystem. Also, unlike most other R packages for statistical modeling which store the whole dataset into memory, LIBMF (and hence Recosystem) is much hard-disk-based. The dataset is not loaded into memory at one time, but rather converted into a temporary binary file. Similarly, the constructed model which contains information for prediction is stored in the hard disk. Finally, prediction result is also not in memory but written into a file. That is to say, Recosystem will have a comparatively small memory usage.

The usage of Recosystem is quite simple, mainly consisting of four steps:

1. Create a model object by calling Reco().
2. Call methods convert_train() and convert_test() to convert data files in text mode into binary form.
3. Train the model by calling train() method. A number of parameters can be set inside the function.
4. Use the predict() method to compute predictions and write results into hard disk.

Below is an example on some simulated data:

library(Recosystem)
trainset = system.file("dat", "smalltrain.txt", package = "Recosystem")
testset = system.file("dat", "smalltest.txt", package = "Recosystem")
r = Reco()
r$convert_train(trainset)

## Converting...done.  0.01
## binary file generated at /tmp/Rtmpw21wCv/smalltrain.txt.bin

r$convert_test(testset)

## Converting...done.  0.01
## binary file generated at /tmp/Rtmpw21wCv/smalltest.txt.bin

r$train(opts = list(dim = 100, niter = 100,
                    cost.p = 0.001, cost.q = 0.001))

## Warning: AVX is enabled.
## Reading training data...done.  0.00
## Initializing model...done.  0.00
## iter       time
## 1          0.00
## 2          0.00
## 3          0.00
<output omitted>
## 98         0.03
## 99         0.03
## 100        0.03
## Writing model...done.  0.00
## model file generated at /tmp/Rtmpw21wCv/smalltrain.txt.bin.model

print(r)

## >>> Training set >>>
## 
## number of users   = 1000
## number of items   = 1000
## number of ratings = 10000
## average           = 3.007000
## 
## >>> Testing set >>>
## 
## number of users   = 1000
## number of items   = 1000
## number of ratings = 10000
## average           = 3.005600
## 
## >>> Model >>>
## 
## number of users = 1000
## number of items = 1000
## dimensions      = 100
## lambda p        = 0.001000
## lambda q        = 0.001000
## lambda ub       = -1.000000
## lambda ib       = -1.000000
## gamma           = 0.001000
## average         = 0.000000

outfile = tempfile()
r$predict(outfile)

## Predicting...done.  0.01
## RMSE: 0.991
## output file generated at /tmp/Rtmpw21wCv/file11037ac85e70

## Compare the first few true values of testing data
## with predicted ones
# True values
print(read.table(testset, header = FALSE, sep = " ", nrows = 10)$V3)

##  [1] 3 4 2 3 3 4 3 3 3 3

# Predicted values
print(scan(outfile, n = 10))

##  [1] 3.209904 3.012498 3.058191 3.496680 2.031080 3.241574 2.668896
##  [8] 2.811245 2.026762 3.457333

Detailed help document for each function is available in topics ?Recosystem::Reco, ?Recosystem::convert, ?Recosystem::train and ?Recosystem::predict.

LIBMF utilizes some compiler and CPU features that may be unavailable in some systems. Currently Recosystem mainly supports UNIX-like operating systems, with experimental support for Windows (See section Precompiled packages). To build Recosystem from source, one needs a C++ compiler that supports C++11 standard.

Also, there are some flags in file src/Makevars that may have influential effect on performance. It's strongly suggested to set proper flags according to your type of CPU before compiling the package, in order to achieve the best performance:

• If your CPU doesn't support SSE3 (typically very old CPUs), set

PKG_CXXFLAGS = -DNOSSE

in the src/Makevars file.

• If SSE3 is supported (a list of supported CPUs), set

PKG_CXXFLAGS = -msse3

• If not only SSE3 is supported but also AVX (a list of supported CPUs), set

PKG_CXXFLAGS = -DUSEAVX -mavx

After editing the Makevars file, run R CMD INSTALL Recosystem on the package source directory to install Recosystem.

Below are the links for some precompiled binary packages for testing:

• Recosystem 0.1 - Windows
• Recosystem 0.1 - Ubuntu 14.04 (64-bit)
• Recosystem 0.1 - Fedora 20 (64-bit)