Recommendation Systems Theory and Coding in C++

Overview

What you'll learn

• Implement baseline recommendation models and similarity measures in C++
• Build collaborative filtering systems utilizing explicit and implicit feedback
• Apply matrix factorization and Alternating Least Squares (ALS) to recommendation data
• Create content-based models from item features and user profiles
• Use factorization machines to produce personalized recommendations
• Evaluate recommendations using coverage, serendipity, novelty, and diversity
• Compare recommendation approaches to select suitable methods for different use cases

Advantages and disadvantages

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Before you start

Beginner: Coding and Data Algorithms (C++) Beginner: Feature Engineering (C++) Intermediate: Machine Learning Model Development (C++) Beginner: Model Validation and Selection (C++)