You're reading from 50 Algorithms Every Programmer Should Know Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781803247762

Length 538 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Data Structures and Algorithms

Author (1):

Imran Ahmad

View More author details

Table of Contents (22) Chapters

Preface

1. Section 1: Fundamentals and Core Algorithms FREE CHAPTER

2. Overview of Algorithms

3. Data Structures Used in Algorithms

4. Sorting and Searching Algorithms

5. Designing Algorithms

6. Graph Algorithms

7. Section 2: Machine Learning Algorithms

8. Unsupervised Machine Learning Algorithms

9. Traditional Supervised Learning Algorithms

10. Neural Network Algorithms

11. Algorithms for Natural Language Processing

12. Understanding Sequential Models

13. Advanced Sequential Modeling Algorithms

14. Section 3: Advanced Topics

15. Recommendation Engines

16. Algorithmic Strategies for Data Handling

17. Cryptography

18. Large-Scale Algorithms

19. Practical Considerations

20. Other Books You May Enjoy

21. Index

Recommendation Engines

The best recommendation I can have is my own talents, and the fruits of my own labors, and what others will not do for me, I will try and do for myself.

—18–19th-century scientist John James Audubon

Recommendation engines harness the power of available data on user preferences and item details to offer tailored suggestions. At their core, these engines aim to identify commonalities among various items and understand the dynamics of user-item interactions. Rather than just focusing on products, recommendation systems cast a wider net, considering any type of item – be it a song, a news article, or a product – and tailoring their suggestions accordingly.

This chapter starts by presenting the basics of recommendation engines. Then, it discusses various types of recommendation engines. In the subsequent sections of this chapter, we’ll explore the inner workings of recommendation systems. These systems...