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Let R talk to Myrrix. Myrrix is a complete, real-time, scalable clustering and recommender system, evolved from Apache Mahout.
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README.md

README.md

Myrrix-R-interface

Let R talk to Myrrix. Myrrix is a complete, real-time, scalable clustering and recommender system, evolved from Apache Mahout.

For more information about Myrrix, go to the Myrrix website: http://myrrix.com.

The R packages allow to build recommendation engines using Myrrix and interact with it. An example is shown below.

Install Myrrixjars and Myrrix

To start up building recommendation engines, install the R packages Myrrixjars and Myrrix as follows.

library(devtools)
install.packages("rJava")
install.packages("ffbase")
install_github("Myrrix-R-interface", "jwijffels", subdir="/Myrrixjars/pkg")
install_github("Myrrix-R-interface", "jwijffels", subdir="/Myrrix/pkg")

Running Myrrix

The following example shows the basic usage on how to use Myrrix to build a local recommendation engine. It uses the audioscrobbler data available on the Myrrix website.

library(Myrrix)

## Download example dataset
inputfile <- file.path(tempdir(), "audioscrobbler-data.subset.csv.gz")
download.file(url="http://dom2bevkhhre1.cloudfront.net/audioscrobbler-data.subset.csv.gz", destfile = inputfile)

## Set hyperparameters
setMyrrixHyperParameters(params=list(model.iterations.max = 2, model.features=10, model.als.lambda=0.1))
x <- getMyrrixHyperParameters(parameters=c("model.iterations.max","model.features","model.als.lambda"))
str(x)

## Build a model which will be stored in getwd() and ingest the data file into it
recommendationengine <- new("ServerRecommender", localInputDir=getwd())
ingest(recommendationengine, inputfile)
await(recommendationengine)

## Get all users/items and score alongside the recommendation model
items <- getAllItemIDs(recommendationengine)
users <- getAllUserIDs(recommendationengine)
estimatePreference(recommendationengine, userID=users[1], itemIDs=items[1:20])
estimatePreference(recommendationengine, userID=users[10], itemIDs=items)
mostPopularItems(recommendationengine, howMany=10L)
recommend(recommendationengine, userID=users[5], howMany=10L)
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