Book Image

Machine Learning in Java

By : Bostjan Kaluza
Book Image

Machine Learning in Java

By: Bostjan Kaluza

Overview of this book

<p>As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.</p> <p>Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering.</p> <p>Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will explore related web resources and technologies that will help you take your learning to the next level.</p> <p>By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.</p>
Table of Contents (19 chapters)
Machine Learning in Java
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
References
Index

Basic concepts


Recommendation engines aim to show user items of interest. What makes them different from search engines is that the relevant content usually appears on a website without requesting it and users don't have to build queries as recommendation engines observe user's actions and construct query for users without their knowledge.

Arguably, the most well-known example of recommendation engine is www.amazon.com, providing personalized recommendation in a number of ways. The following image shows an example of Customers Who Bought This Item Also Bought. As we will see later, this is an example of collaborative item-based recommendation, where items similar to a particular item are recommended:

An example of recommendation engine from www.amazon.com.

In this section, we will introduce key concepts related to understanding and building recommendation engines.

Key concepts

Recommendation engine requires the following four inputs to make recommendations:

  • Item information described with attributes...