【Data Science Project】Music Recommender System using Pyspark

Photo by Marius Masalar on Unsplash

Introduction

Recommender systems are ubiquitous in today’s digital landscape. Platforms like Amazon and Spotify leverage these systems to suggest products or music based on user preferences. This project focuses on creating a Music Recommender System using the ALS (Alternating Least Squares) algorithm in PySpark. Prior knowledge of ALS is recommended.

Objectives

1. Set up Google Colab for distributed data processing.
2. Aggregate a PySpark DataFrame to prepare data for the machine learning model.
3. Use StringIndexer to convert categorical columns (User ID and Track) into unique integral columns.
4. Create an ALS model for the Recommender System.

1. Loading the Dataset

This project aims to familiarize you with recommender systems, which are widely used, such as on Amazon or Spotify, employing algorithms like matrix factorization. Our focus will be on creating a recommender system for Last FM, using PySpark’s ALS algorithm.