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Content-Based Recommendation Systems using JS

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Overview

In this course, learners will dive into content-based recommendation systems, focusing on 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

Merging Arrays of Objects by Author ID
Enhancing Data Processing with Playtime Feature
Creating a Unified DataFrame for Music Recommendations

Unit 2: Content Based Recommendations

Adding a New Movie to Item Profiles for Recommendation System
Exploring User Preferences in Content-Based Recommendation Systems
Correcting Dot Product Calculation for Accurate Recommendations
Sorting Movie Recommendations by Similarity Scores
Song Recommendation System Using Content-Based Filtering

Unit 3: Advanced Content Recommendations

Genre Mapping with One-Hot Encoding
Updating User Preferences for Rock Genre Enthusiast
Incorporating Duration into Recommendation System
Linear Regression Model for Music Recommendation System

Unit 4: Preparing Data for Recommendations

Loading JSON Data for Factorization Machines
Creating Dummy Variables for Users and Tracks
Calculating Genre Similarity with Cosine Similarity
Enhancing Data Processing with Track Duration Feature
Consolidating Features for Factorization Machines

Unit 5: Factorization Machines in JavaScript

Enhancing Factorization Machine Model with Linear Terms
Extend Dataset for Factorization Machine Analysis
Enhance SimpleFactorizationMachine Predict Method
Factorization Machine Model Implementation and Evaluation
Hyperparameter Tuning for Factorization Machines

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