Recommendation Systems Theory and Coding in Go

This beginner-level course path introduces the algorithms and concepts behind recommendation systems. You will begin with baseline prediction models and similarity measures used to estimate user preferences.

You will then implement user-based and item-based collaborative filtering, matrix factorization, and Alternating Least Squares (ALS). You will also explore content-based recommendations through feature engineering, user and item profiles, and factorization machines.

Through practical Go examples, you will learn to handle explicit and implicit feedback, and evaluate model performance using metrics like RMSE and Mean Rank. This path is suited to learners with a solid foundation in Go and machine learning principles who want to build personalized recommendation tools.

Syllabus

Implement baseline prediction and similarity-based recommendation models in Go
Build user-based and item-based collaborative filtering systems
Apply matrix factorization and Alternating Least Squares (ALS/IALS) techniques
Engineer features and create user and item profiles
Develop content-based models and prepare datasets for factorization machines
Process both explicit rating matrices and implicit user feedback
Evaluate recommendation quality using relevant performance metrics like RMSE and Mean Rank