These series of tutorials will walk through each components of PredictionIO. We will demonstrate how to develop your machine learning algorithms and prediction engines, deploy them and serve real time prediction queries, develop your metrics to run offline evaluations, and improve prediction engine by using multiple algoritms.
You need to build PredictionIO from source in order to build your own engine. Please follow instructions to build from source here.
Let‘s build a simple Java single machine recommendation engine which predicts item’s rating value rated by the user. MovieLens 100k data set will be used as an example.
Execute the following command to download MovieLens 100k to data/ml-100k/.
$ cd $PIO_HOME/examples $ src/main/java/recommendations/fetch.sh
where $PIO_HOME is the root directory of the PredictionIO code tree.
In this first tutorial, we will demonstrate how to build an simple Item Recommendation Engine with the DataSource and Algorithm components. You can find all sources code of this tutorial in the directory src/main/java/recommendations/tutorial1/.
Let's begin with implementing a new Engine with Data and Algorithm.