Content-Based Filtering

Content-based filtering is a recommendation system technique that suggests items to users based on the attributes or features of the items and the user's past preferences. It analyzes item characteristics (e.g., genre, keywords, metadata) and matches them to a user's profile built from their interaction history, such as ratings or purchases. This approach is widely used in applications like movie, music, or product recommendations to personalize user experiences.

Also known as: Content-Based Recommendation, Content Filtering, CBF, Attribute-Based Filtering, Feature-Based Filtering

Why learn Content-Based Filtering?

Developers should learn content-based filtering when building recommendation systems that require personalization without relying on other users' data, making it suitable for cold-start scenarios where new users or items have limited interaction history. It is particularly useful in domains like e-commerce, streaming services, or news aggregation, where item features are well-defined and user preferences can be inferred from explicit feedback. This method helps improve user engagement by delivering relevant content based on individual tastes.

Learning Resources

• docs: Recommender Systems: Content-Based Filtering
• tutorial: Content-Based Filtering in Python
• course: Coursera: Recommender Systems Specialization
• video: Content-Based Filtering Explained
• book: Book: Recommender Systems Handbook