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
The Rationale Behind Collaborative Filtering
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- Recommender Systems: The Textbook, Charu C. Aggarwal, 2016 (Springer) DOI: 10.1007/978-3-319-29659-3 - This textbook provides a comprehensive introduction to recommender systems, including detailed explanations of collaborative filtering techniques, their underlying principles, and the distinction from content-based methods.
- GroupLens: An Open Architecture for Collaborative Filtering of Netnews, Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, John Riedl, 1994 Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (ACM Press) DOI: 10.1145/192844.192905 - A seminal paper that introduced one of the first widely recognized collaborative filtering systems, GroupLens, establishing user-based recommendation methods.
- Item-based collaborative filtering recommendation algorithms, Badrul Sarwar, George Karypis, Joseph A Konstan, John Riedl, 2001 Proceedings of the 10th international conference on World Wide Web (Association for Computing Machinery) DOI: 10.1145/371920.372071 - This paper introduces and empirically evaluates item-based collaborative filtering, a method that forms the other side of neighborhood-based CF by focusing on item similarities derived from user ratings.
- Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, Gediminas Adomavicius, Alexander Tuzhilin, 2005 IEEE Transactions on Knowledge and Data Engineering, Vol. 17 (IEEE) DOI: 10.1109/TKDE.2005.99 - A highly cited survey that provides a comprehensive overview of recommender system techniques, including a detailed discussion of collaborative filtering's principles, types, and benefits like serendipity.