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Movie Recommendation System using Apache Spark deployed on EMR Cluster

This repo has the code base for Movie Recommendation System developed in Apache Spark with Scala. There are a couple of ways to develop recommendation engines that typically produce a list of recommendations.

  • Content based filtering approach: Two similarity metrics are used in form of Cosine similarity based on Euclidean Distance metric and Jaccard similarity based on set theory These two metrics find the similarities between two movies based on the ratings received by similar users. (Folder: local_deploy ; Code: moviesimilarity.scala)
  • Model based collaborative filtering approach: In this technique, users and products are described by a small set of factors, also called latent factors. The LFs are then used to predict the missing entries. The Alternating Least Squares algorithm is used to learn these LFs. This filtering approach is commonly used for real time movie recommendations. (Folder: aws_deploy ; Code: alsrecommendation.scala)

About Data

The dataset is downloaded from MovieLens. It contains 25,000,095 ratings from 162,541 users on 62,423 movies.

System Requirements

  • IntelliJ Project Setup Information:
    • JDK Version: 1.8.0_271-b09
    • Scala Version: 2.11.12
    • SBT Version: 1.14.0
  • Check build.sbt to see Spark project dependencies.
  • AWS EMR Cluster Setup Information:
    • Software Configuration:
      • Release: emr-5.31.0
      • Applications: Spark 2.4.6 on Hadoop 2.10.0 YARN and Zeppelin 0.8.2
    • Hardware Configuration: Instance Type: 4 General Purpose m5.xlarge (1 Master and 3 Core nodes)

Deployment Workflow

Workflow

Note:

Code base is copied from PacktPublishing/Scala-Machine-Learning-Projects.