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Building a Machine Learning Recommendation System

Chapter 1: Foundations of Recommendation Systems

What are Recommendation Systems?

Taxonomy of Recommender Engines

Understanding User-Item Interaction Data

Implicit vs. Explicit Feedback

The Cold-Start Problem

Preparing Your Development Environment

Practice: Loading and Inspecting a Dataset

Chapter 2: Content-Based Filtering

The Mechanics of Content-Based Recommenders

Creating Item Profiles from Metadata

Vectorizing Text Data with TF-IDF

Computing Similarity with Cosine Distance

Generating User Profiles

Producing Content-Based Recommendations

Hands-on Practical: Building a Movie Recommender

Chapter 3: Neighborhood-Based Collaborative Filtering

The Rationale Behind Collaborative Filtering

The User-Item Interaction Matrix

User-Based vs. Item-Based Approaches

Finding Similar Users and Items with k-Nearest Neighbors

Calculating Similarity Metrics

Making Predictions with Weighted Averages

Addressing Sparsity in the Interaction Matrix

Hands-on Practical: Implementing an Item-Based Filter

Chapter 4: Model-Based Collaborative Filtering with Matrix Factorization

From Neighborhoods to Latent Factor Models

Introduction to Matrix Factorization

Singular Value Decomposition (SVD) for Recommendations

Optimization with Stochastic Gradient Descent (SGD)

Regularization to Prevent Overfitting

Using Libraries for Matrix Factorization

Hands-on Practical: Generating Recommendations with SVD

Chapter 5: Evaluating Recommendation Systems

The Importance of Recommender Evaluation

Offline vs. Online Evaluation Methods

Splitting Data for Recommender Evaluation

Prediction Accuracy Metrics: RMSE and MAE

Ranking Metrics: Precision and Recall at K

Mean Average Precision (MAP)

Normalized Discounted Cumulative Gain (NDCG)

Hands-on Practical: Measuring Model Performance

Chapter 6: Constructing a Hybrid Recommendation System

Limitations of Single-Algorithm Systems

Combining Recommender Models

Weighted Hybridization

Switching and Mixed Hybridization Techniques

Feature Combination Methods

Building a System that Blends Content and Collaborative Signals

Hands-on Practical: A Weighted Hybrid Movie Recommender

Building a Machine Learning Recommendation System

A practical guide to designing, building, and evaluating recommendation systems. This course covers the fundamental algorithms, including content-based and collaborative filtering, and advances to model-based techniques like matrix factorization. You will learn to process user-item interaction data, generate personalized recommendations, and measure the performance of your systems using industry-standard metrics. The focus is on a hands-on approach, enabling you to construct functional recommenders from the ground up.

Prerequisites Python and ML fundamentals

Level:

Intermediate

Implement Content-Based Filtering
Construct a recommendation system that suggests items based on their properties and user profiles.
Build Collaborative Filtering Models
Develop neighborhood-based recommenders that leverage user-item interaction patterns.
Apply Matrix Factorization
Use techniques like Singular Value Decomposition (SVD) to create powerful model-based recommenders.
Evaluate Recommender Performance
Measure and compare the effectiveness of different recommendation models using offline metrics like precision, recall, and NDCG.
Construct a Hybrid System
Combine multiple recommendation strategies to improve performance and address common issues like the cold-start problem.

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