Content-based Filtering System

Content-based filtering is a technique used in Recommender Systems to provide personalized recommendations by analyzing the attributes of items and the user's preferences. It recommends items similar to those the user has liked in the past.


  1. Item Representation: The system represents items using their attributes, such as keywords, genres, or features.
  2. User Profile Creation: It creates a profile for each user based on their past interactions and preferences.
  3. Similarity Calculation: The system calculates the similarity between the user profile and the item attributes.
  4. Recommendation Generation: Based on the similarity scores, the system generates recommendations for the user.


  • Components:
    • Item Attributes: These are the characteristics or features of the items being recommended, such as genre, keywords, or metadata.
    • User Profile: This is a representation of the user's preferences and past interactions with items.
  • Content-based filtering is based on the idea of recommending items that are similar to those that a user has liked in the past.
  • It does not rely on the preferences of other users, making it suitable for providing recommendations for new or less popular items.
  • Content-based filtering is commonly used in e-commerce, music streaming, and news recommendation systems to provide personalized suggestions to users based on item attributes and user preferences.