Machine Learning
Content-Based Recommendation Systems
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.
Python
See path
5 lessons
22 practices
4 hours
Badge for Machine Learning Model Development,
Machine Learning Model Development
Lessons and practices
Lesson 1: Content Features Extraction in Recommendation Systems
Link Videos with Channels Using Dataframes
Enhance Features with Playtime
Unifying Game Data for Recommendations
Lesson 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
Lesson 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
Lesson 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
Lesson 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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