Tree and Graph Data Structures for Recommendation Engines

Modern applications from social networks to streaming platforms rely on connected data to deliver highly relevant suggestions. Understanding how trees and graphs represent these complex, real-world relationships is key to building effective search and recommendation systems. By learning to navigate these structures, you unlock the ability to solve complex routing, grouping, and discovery problems. In this text-only course, you will transition from writing basic linear code to modeling complex, interconnected data. You will understand how to structure hierarchical information and network relationships, enabling you to build the core search algorithms that power modern discovery engines. What you'll learn: - Understand foundational tree and graph terminologies, properties, and core concepts. - Implement depth-first search (DFS) and breadth-first search (BFS) algorithms systematically. - Analyze how networks and recommendation systems map relationships using graph structures. - Apply modern coding standards, including type hints and clean data structures, to write readable algorithm implementations. - Explore how graph search fundamentals translate to modern recommendation patterns and vector-based search concepts. This course starts with basic definitions and foundational structures before guiding you through search traversal algorithms and their practical application in recommendation logic. You will learn by reading clear explanations, studying structured code examples, and completing written analysis exercises. This course is designed for beginning developers and computer science enthusiasts who want to understand data structures. No prior experience with graphs or advanced algorithms is required. Start exploring the power of graph-based search and recommendation algorithms today.