Recommendation Systems Theory and Coding

Overview

What you'll learn

• Implement baseline predictors and similarity measures in Python
• Build user-based and item-based collaborative filtering models
• Apply matrix factorization and Alternating Least Squares to recommendation tasks
• Model implicit feedback for personalized recommendations
• Develop content-based models using Factorization Machines and DSSM
• Evaluate systems using coverage, novelty, diversity, and serendipity metrics

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Before you start

Beginner: Coding and Data Algorithms (Numpy, Python) Intermediate: Machine Learning Model Development (Numpy, Python) Beginner: Model Validation and Selection (Numpy, Python)