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Content-Based and Model-Based Recommendations in Go

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Overview

In this course, learners will dive into content-based recommendation systems, focusing on feature engineering, user and item profiling, and factorization machines. These approaches utilize item features and user profiles to make recommendations. The course provides hands-on coding examples to demonstrate how to develop content-based models that harness rich data for personalized recommendations.

Syllabus

Unit 1: Content Based Recommendations in Go

Merging Data Structures in Go
Adding Playtime Feature to Data Processing
Merging Game Data for Recommendation Features

Unit 2: Content Based Recommendations

Adding New Movies to Recommendation System
Exploring User Preferences in Content-Based Recommendation Systems
Fix the Dot Product Calculation in Similarity Function
Sorting Movie Recommendations by Similarity Scores
Content-Based Song Recommendation System

Unit 3: Advanced Content Recommendations

Implementing Cosine Similarity for Genre Matching
Building Genre Vector Mappings
Building Complete Track Recommendation Pipeline
Creating Scored Track Recommendations

Unit 4: Preparing Data for Recommendations

Loading JSON Data Files in Go
Creating Dummy Variables for Users and Tracks
Calculate Genre Similarity Using Cosine Similarity
Adding Track Duration Feature to Data Matrix
Building a Data Matrix for Factorization Machines

Unit 5: Factorization Machines in Go

Calculate Interaction Terms in Factorization Machine
Expanding the Dataset with Additional Users and Tracks
Implementing the Predict Method for Factorization Machine
Complete Factorization Machine Implementation
Hyperparameter Tuning for Factorization Machines

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