Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

Key issues with recommendation systems


There are three key issues with recommender systems in general:

  1. Gathering known input data
  2. Predicting unknown from known ratings
  3. Evaluating Prediction methods

Gathering known input data

The first interim milestone in building a recommendation system is to gather the input data, that is, customers, products, and the relevant ratings. While you already have customers and products in your CRM and other systems, you would like to get the ratings of the products from the users. There are two methods to collect product ratings:

  • Explicit: Explicit ratings means the users would explicitly rate a particular item, for example, a movie on Netflix, a book/product on Amazon, and so on. This is a very direct way to engage with users and it typically provides the highest quality data. In real life, despite the incentives given to rate an item, very few users actually leave ratings for the products. Getting explicit ratings is therefore not scalable for any meaningful prediction...