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)

User specific recommendations


During the remainder of this chapter, we will focus on user-specific ratings. Let's start by considering a model of the recommendation system.

Let's assume:

C = Set of customers.

I = Set of items (could be movies, books, news items, and so on).

R = Set of ratings. This is an ordered set, where higher numbers indicate the high likeness of a particular item, whereas the lower value indicates a low likeness of a particular item. Generally this is represented by a real value between 0 and 1.

Let's define a utility function u, which looks at every pair of customers and items and maps it to a specific rating:

u: C * I → R

Let's give an example of a utility matrix, for a set of movies and users:

Godfather I

Godfather II

Good Will Hunting

A Beautiful Mind

Roger

1

0.5

Aznan

1

0.7

0.2

Fawad

0.9

0.8

0.1

Adrian

1

0.8

A utility matrix is generally a sparse matrix, as users rate fewer movies than they watch. The areas where ratings are missing can be either...