Recommender Systems

  1. Data Foundations and Preprocessing
    1. Types of User Feedback
      1. Explicit Feedback
        1. Ratings
          1. Likes and Dislikes
            1. Reviews and Comments
              1. Bookmarks and Favorites
              2. Implicit Feedback
                1. Clicks
                  1. Purchase History
                    1. Viewing Time
                      1. Search Queries
                        1. Browsing Patterns
                          1. Add-to-Cart Events
                            1. Download Behavior
                          2. Data Representation
                            1. User-Item Interaction Matrix
                              1. Structure and Notation
                                1. Binary vs Real-Valued Entries
                                  1. Sparse Matrix Representation
                                  2. Properties of Interaction Matrix
                                    1. Sparsity Characteristics
                                      1. Density Patterns
                                        1. Temporal Evolution
                                      2. Data Collection and Storage
                                        1. Logging User Interactions
                                          1. Event Tracking Systems
                                            1. Data Pipeline Design
                                            2. Privacy Considerations
                                              1. Data Anonymization
                                              2. Data Storage Solutions
                                                1. Relational Databases
                                                  1. NoSQL Databases
                                                    1. Distributed Storage Systems
                                                  2. Data Preprocessing and Cleaning
                                                    1. Handling Missing Values
                                                      1. Imputation Methods
                                                        1. Ignoring Missing Data
                                                        2. Noise Reduction
                                                          1. Outlier Detection
                                                            1. Filtering Noisy Interactions
                                                            2. Data Transformation and Normalization
                                                              1. Scaling Ratings
                                                                1. Binarization of Implicit Data
                                                                  1. Feature Engineering for Items and Users

                                                              Previous

                                                              1. Introduction to Recommender Systems

                                                              Go to top

                                                              Next

                                                              3. Content-Based Filtering