This is an example code to build recommenders with/without Spark.

Original data is Amazon Movie Reviews provided by Stanford Network Analysis Project .

This repo includes:

• Parse amazon movie reviews data and amazon meta data.
• Create tables with Spark.
• Visualize basic stats of review data.
• Build recommender with ALS from spark.ml.
• Build recommender with Factorization Machine using fastFM.

1. Open workbench and run setup.sh for data preparation on the terminal. It takes about 20 minutes to parse data and put JSON into HDFS
2. Run data-preparation.py to create tables
3. Run data-visualization.py to show summary of data
4. Run build-recommender.py for building a Spark ALS model
5. Run fastfm-recommender.py for building a Factorization Machines model