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Content-Based Recommendation Systems

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Overview

Dive into content-based recommendation systems, focusing on feature extraction, similarity measures, and factorization machines. You will learn to utilize item features and user profiles to build personalized models. This course provides hands-on C++ examples, progressing from simple similarity methods to advanced factorization techniques for robust, data-driven recommendations.

Syllabus

Unit 1: Content Based Recommendation Systems

Merging Track and Author Dataframes by Author ID
Adding a Playtime Feature to the Content Features Table
Building a Unified Content Features DataFrame for Recommendations

Unit 2: Content Based Recommendations

Adding a New Track to the Recommendation System
Exploring User Preferences in Content-Based Recommendation Systems
Fixing the Similarity Calculation in a Recommendation System
Sorting Recommendations by Similarity Score
Content-Based Song Recommendation System

Unit 3: Advanced Content Recommendations

One-Hot Encoding Genres for Recommendation Features
Adapting User Preferences for Rock-Focused Recommendations
Adding a Duration Feature and Standardization to the Recommendation System
Predicting Song Ratings with Linear Regression

Unit 4: Preparing Data for Factorization Machines

Loading JSON Data for Recommendation Systems
Creating Dummy Variables for Users and Tracks
Calculating Genre Similarity with Cosine Similarity for User-Track Interactions
Add Track Duration Feature to the Content Features DataFrame
Building a Feature Matrix for a Recommendation System

Unit 5: Factorization Machines in C++

Implement Linear Terms Calculation in the Factorization Machine Model
Expanding the Dataset with Additional Users and Items
Implement the Predict Method for the SimpleFactorizationMachine
Implementing a Factorization Machine and Evaluating with Mean Absolute Error
Hyperparameter Tuning for Factorization Machines

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