Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Content-based recommendation systems


With the advancement of rich, performant technology and more focus on data-driven analytics, recommendation systems are gaining popularity. Recommendation systems are components that provide the most relevant information to end users based on their behavior in the past. The behavior can be defined as a user's browsing history, purchase history, recent searches, and so on. There are many different types of recommendation systems. In this section, we will keep our focus on two categories of recommendation engines: collaborative filtering and content-based recommendation. 

Content-based recommendation systems are the type of recommendation engines that recommend items that are similar to items the user has liked in the past. The similarity of items is measured using features associated with an item. Similarity is basically a mathematical function that can be defined by a variety of algorithms. These types of recommendation systems match user profile attributes...