Recommender Systems

  1. Practical Challenges and System Design
    1. Cold-Start Problem
      1. New User Cold-Start
        1. Onboarding Questionnaires
          1. Demographic-Based Recommendations
          2. New Item Cold-Start
            1. Content-Based Initialization
              1. Promotion Strategies
                1. Expert Recommendations
                2. New System Cold-Start
                  1. Bootstrap Strategies
                  2. Mitigation Strategies
                    1. Hybrid Approaches
                      1. Active Learning
                        1. Transfer Learning
                      2. Scalability Challenges
                        1. Handling Large User and Item Sets
                          1. Distributed Computing
                            1. Parallelization Strategies
                              1. Approximation Algorithms
                              2. Real-time Recommendation Generation
                                1. Caching Strategies
                                  1. Precomputation
                                    1. Incremental Model Updates
                                    2. Memory and Storage Constraints
                                      1. Sparse Matrix Storage
                                        1. Model Compression
                                      2. Data Sparsity
                                        1. Impact on Model Performance
                                          1. Techniques for Addressing Sparsity
                                            1. Matrix Factorization
                                              1. Data Augmentation
                                                1. Regularization
                                              2. Explainability and Interpretability
                                                1. Importance of Explanations
                                                  1. Types of Explanations
                                                    1. Feature-Based Explanations
                                                      1. Neighbor-Based Explanations
                                                        1. Example-Based Explanations
                                                        2. Generating Explanations
                                                          1. Post-hoc Explanations
                                                            1. Inherently Interpretable Models
                                                            2. User Trust and Transparency
                                                            3. Fairness, Bias, and Ethics
                                                              1. Types of Bias
                                                                1. Popularity Bias
                                                                  1. Position Bias
                                                                    1. Selection Bias
                                                                    2. Fairness Across User Groups
                                                                      1. Demographic Parity
                                                                        1. Equal Opportunity
                                                                        2. Fairness for Item Providers
                                                                          1. Exposure Fairness
                                                                            1. Revenue Fairness
                                                                            2. Filter Bubbles and Echo Chambers
                                                                              1. Diversity Promotion
                                                                                1. Exploration vs Exploitation
                                                                                2. Privacy Concerns
                                                                                  1. Data Protection
                                                                                    1. Differential Privacy
                                                                                    2. Ethical Recommendation Practices
                                                                                      1. Responsible AI Guidelines
                                                                                    3. System Architecture
                                                                                      1. Offline Components
                                                                                        1. Model Training Pipelines
                                                                                          1. Batch Processing Systems
                                                                                            1. Feature Engineering Pipelines
                                                                                            2. Near Real-time Components
                                                                                              1. Streaming Data Processing
                                                                                                1. Incremental Learning
                                                                                                2. Online Components
                                                                                                  1. Real-time Serving
                                                                                                    1. API Design
                                                                                                      1. Latency Optimization
                                                                                                      2. Infrastructure Considerations
                                                                                                        1. Monitoring and Logging
                                                                                                          1. A/B Testing Framework
                                                                                                            1. Model Deployment
                                                                                                              1. Performance Optimization

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                                                                                                          7. Evaluation of Recommender Systems

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                                                                                                          1. Introduction to Recommender Systems