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Content-Based Recommendation Systems

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Overview

In this course, learners will dive into content-based recommendation systems, focusing on factorization machines and Deep Structured Semantic Models (DSSM). 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 Features Extraction in Recommendation Systems

Link Videos with Channels Using Dataframes
Enhance Features with Playtime
Unifying Game Data for Recommendations

Unit 2: Introduction to Content-Based Recommendations

Adding Movies to Improve Recommendations
Adjust User Preferences for Recommendations
Fixing Similarity Computation Bug
Sorting Movie Recommendations by Score
Building a Music Recommendation System

Unit 3: Setting Content-Based Recommendations Baseline with Linear Regression

Expand Genre Mapping with Vectors
Adjust User Preferences for Recommendations
Data Feature Integration Made Easy
Predict Song Ratings with Regression

Unit 4: Preparing Dataset for Factorization Machines

Loading Data from JSON Files
Creating Dummy Variables for Interaction
Calculating Genre Similarity Efficiently
Enhance Data Features with Ease
Bring Features Together for Recommendations

Unit 5: Implementing Factorization Machines

Factorization Machine Linear Terms
Expanding Your Dataset Skills
Complete the Prediction Algorithm
Build and Train a Factorization Machine
Fine-Tune Model Performance

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