Learn implementation methods for personalization, from the fundamentals of recommendation systems to collaborative filtering, matrix factorization, and deep learning-based recommendation techniques
Series Overview
This series is a practical educational content consisting of 4 chapters that enables you to learn the theory and implementation of Recommendation Systems from basics to advanced levels in a step-by-step manner.
Recommendation Systems are machine learning technologies that suggest optimal products, content, and information based on user preferences and behavioral history. Analyzing user-item similarity through collaborative filtering, extracting latent factors through matrix factorization (SVD, ALS), feature matching through content-based filtering, integrating multiple algorithms through hybrid methods, advanced personalization through deep learning (Neural Collaborative Filtering, DeepFM, Two-Tower Model) - these technologies are implemented in global platforms such as Amazon, Netflix, YouTube, and Spotify, and have become essential skills in all fields including e-commerce, video streaming, music streaming, and news distribution. This series provides practical knowledge necessary for real-world applications, including understanding evaluation metrics (RMSE, Precision@K, nDCG), addressing cold start problems, and validating recommendation accuracy through A/B testing.
Features:
- ✅ From Theory to Practice: Systematic learning from fundamentals of recommendation algorithms to the latest deep learning methods
- ✅ Implementation Focus: Over 35 executable Python/scikit-learn/PyTorch code examples
- ✅ Practical Orientation: Practical recommendation methods based on real examples from Netflix, Amazon, and YouTube
- ✅ Latest Technology Compliance: Up-to-date methods in collaborative filtering, matrix factorization, and deep learning recommendations
- ✅ Practical Applications: Implementation for e-commerce, video streaming, music recommendation, and news distribution
Total Learning Time: 5-6 hours (including code execution and exercises)
How to Learn
Recommended Learning Order
For Beginners (no prior knowledge of recommendation systems):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 (all chapters recommended)
- Duration: 5-6 hours
For Intermediate Learners (with machine learning experience):
- Chapter 1 → Chapter 2 → Chapter 4
- Duration: 4-5 hours
Strengthening Specific Topics:
- Recommendation System Fundamentals and Evaluation Metrics: Chapter 1 (focused learning)
- Collaborative Filtering and Matrix Factorization: Chapter 2 (focused learning)
- Content-Based and Hybrid Recommendations: Chapter 3 (focused learning)
- Deep Learning Recommendations: Chapter 4 (focused learning)
- Duration: 70-90 minutes/chapter
Chapter Details
Chapter 1: Recommendation System Fundamentals
Difficulty: Intermediate
Reading Time: 70-80 minutes
Code Examples: 8
Learning Content
- What are Recommendation Systems - Definition, business value, major application areas
- Types of Recommendation Tasks - Rating prediction, ranking, Top-N recommendation
- Evaluation Metrics - RMSE, MAE, Precision@K, Recall@K, nDCG
- Dataset Structure - User-item matrix, implicit and explicit feedback
- Cold Start Problem - Approaches for new users and new items
Learning Objectives
- ✅ Understand basic concepts and business value of recommendation systems
- ✅ Explain types of recommendation tasks
- ✅ Calculate and interpret major evaluation metrics
- ✅ Understand the structure of user-item matrices
- ✅ Explain the cold start problem and its solutions
Chapter 2: Collaborative Filtering
Difficulty: Intermediate
Reading Time: 80-90 minutes
Code Examples: 10
Learning Content
- Principles of Collaborative Filtering - User similarity and item similarity
- User-based CF - User similarity, neighborhood selection, rating prediction
- Item-based CF - Item similarity, scalability
- Matrix Factorization (SVD) - Latent factor model, dimensionality reduction, rating prediction
- ALS (Alternating Least Squares) - Handling implicit feedback
Learning Objectives
- ✅ Understand the principles of collaborative filtering
- ✅ Implement User-based CF and Item-based CF
- ✅ Implement similarity calculations (cosine similarity, Pearson correlation)
- ✅ Implement matrix factorization using SVD
- ✅ Understand and implement the ALS algorithm
Chapter 3: Content-Based and Hybrid
Difficulty: Intermediate
Reading Time: 70-80 minutes
Code Examples: 9
Learning Content
- Content-Based Filtering - Item features, TF-IDF, profile construction
- Feature Engineering - Categorical features, text features, numerical features
- Hybrid Recommendation - Integration of collaborative filtering + content-based
- Weighted Integration - Linear combination, switching, cascade
- Cold Start Solutions - Utilizing content information
Learning Objectives
- ✅ Understand the principles of content-based filtering
- ✅ Implement feature extraction using TF-IDF
- ✅ Build user profiles
- ✅ Implement hybrid recommendation methods
- ✅ Address cold start problems
Chapter 4: Deep Learning Recommendations
Difficulty: Advanced
Reading Time: 80-90 minutes
Code Examples: 10
Learning Content
- Neural Collaborative Filtering (NCF) - MLP, GMF, NeuMF
- Embedding Layers - Learning distributed representations of users and items
- DeepFM - FM + Deep Neural Network, feature interactions
- Two-Tower Model - User tower, item tower, efficient inference
- Transformer Recommendations - Self-Attention, sequence recommendations
Learning Objectives
- ✅ Understand the mechanisms of Neural Collaborative Filtering
- ✅ Implement recommendation models using embedding layers
- ✅ Implement DeepFM models
- ✅ Implement Two-Tower Models
- ✅ Understand Transformer-based recommendation models
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain basic concepts and evaluation metrics of recommendation systems
- ✅ Understand the differences between collaborative filtering, content-based, and hybrid methods
- ✅ Explain the principles of matrix factorization (SVD, ALS)
- ✅ Understand the mechanisms of deep learning recommendations (NCF, DeepFM, Two-Tower)
- ✅ Explain the cold start problem and its solutions
Practical Skills (Doing)
- ✅ Implement User-based CF and Item-based CF
- ✅ Implement matrix factorization using SVD and ALS
- ✅ Implement content-based filtering
- ✅ Build hybrid recommendation systems
- ✅ Implement deep learning recommendation models in PyTorch
Application Ability (Applying)
- ✅ Select appropriate recommendation algorithms
- ✅ Evaluate and improve recommendation systems
- ✅ Address cold start problems
- ✅ Apply to e-commerce, video streaming, and music recommendations
- ✅ Validate recommendation accuracy through A/B testing
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Basics: Variables, functions, classes, NumPy/pandas
- ✅ Machine Learning Fundamentals: Supervised learning, evaluation metrics, cross-validation
- ✅ Linear Algebra Basics: Matrix operations, inner products, norms
- ✅ Statistics Basics: Mean, variance, correlation coefficient
- ✅ scikit-learn Basics: Basic model training and evaluation
Recommended (Nice to Have)
- 💡 PyTorch Basics: Tensor operations, model definition, training loop (for Chapter 4)
- 💡 Natural Language Processing Basics: TF-IDF, word embeddings (for Chapter 3)
- 💡 Deep Learning Basics: Neural networks, loss functions, optimization (for Chapter 4)
- 💡 Sparse Matrices: How to use scipy.sparse
- 💡 Data Visualization: matplotlib, seaborn
Recommended Prior Learning:
- 📚 - ML fundamentals - Matrix operations