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
Optimization with Stochastic Gradient Descent (SGD)
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- Matrix Factorization Techniques for Recommender Systems, Yehuda Koren, Robert Bell, Chris Volinsky, 2009 Computer, Vol. 42 (IEEE Computer Society) DOI: 10.1109/MC.2009.263 - This paper introduced and popularized matrix factorization with SGD for recommendation systems, especially after its success in the Netflix Prize. It describes the objective function and iterative optimization process.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - Chapter 8 covers optimization algorithms, including SGD, and various regularization methods. This material is fundamental for understanding the underlying mechanics of iterative model training.
- Matrix Factorization Methods for Recommender Systems, Yehuda Koren, Robert Bell, 2015 (Springer) DOI: 10.1007/978-1-4939-2708-2_5 - This chapter from the Recommender Systems Handbook (2nd Edition) provides a detailed overview of matrix factorization in recommender systems, explaining various models and optimization strategies, including the use of SGD.
- Lecture Notes on Gradient Descent, CS229, Andrew Ng, 2022 - These well-regarded lecture notes offer a clear introduction to gradient descent, providing foundational understanding applicable to SGD optimization in matrix factorization.