Recommender Systems

Recommender systems are a specialized class of machine learning algorithms designed to predict a user's preferences and suggest relevant items, such as products, movies, or articles. By analyzing vast datasets of user behavior (like past ratings, purchases, or clicks) and item attributes, these systems identify patterns to make personalized suggestions. The two primary approaches are collaborative filtering, which leverages the preferences of similar users, and content-based filtering, which recommends items similar to those a user has previously liked. As a practical application of AI, recommender systems are crucial for navigating information overload and are fundamental to the user experience on platforms like Netflix, Amazon, and Spotify.

1. Introduction to Recommender Systems
   1. Defining Recommender Systems
      1. Basic Definition and Purpose
      2. Historical Development
      3. Key Applications and Use Cases
         1. E-commerce Platforms
         2. Streaming Services
         3. Social Media
         4. News and Content Platforms
         5. Search Engines
   2. The Problem of Information Overload
      1. Growth of Digital Content
      2. User Decision Fatigue
      3. The Need for Personalization
   3. Goals of Recommendation
      1. Relevance
         1. Matching User Preferences
         2. Contextual Relevance
      2. Novelty
         1. Introducing Unseen Items
      3. Serendipity
         1. Unexpected but Pleasant Recommendations
      4. Diversity
         1. Avoiding Redundancy
         2. Broadening User Experience
      5. Coverage
         1. Recommending Across the Catalog
   4. Taxonomy of Recommender Systems
      1. Content-Based Filtering
      2. Collaborative Filtering
      3. Hybrid Approaches
      4. Knowledge-Based Systems
      5. Demographic-Based Systems
   5. Key Terminology
      1. Users
         1. User Profiles
         2. User Preferences
      2. Items
         1. Item Attributes
         2. Item Catalog
      3. Ratings and Interactions
         1. Explicit Ratings
         2. Implicit Interactions
         3. Temporal Dynamics