Content-Based Filtering
A recommendation technique that suggests items similar to those a user has previously liked, based on the items' features and attributes rather than other users' behavior.
Why It Matters
Content-based filtering works even for new items with no user history (solving the cold start problem) and provides explainable recommendations.
Example
Recommending a sci-fi thriller book because you previously enjoyed books tagged as sci-fi and thriller, based on genre, author style, and theme similarity.
Think of it like...
Like a sommelier who recommends wines based on the flavors you have told them you enjoy — 'Since you like bold reds, try this Malbec.'
Related Terms
Recommendation System
An AI system that predicts and suggests items a user might be interested in based on their behavior, preferences, and similarities to other users.
Collaborative Filtering
A recommendation technique that predicts a user's interests based on the preferences of similar users. It assumes people who agreed in the past will agree again in the future.
Feature Engineering
The process of selecting, transforming, and creating input variables (features) from raw data to improve model performance. It requires domain knowledge to identify what information is most useful for the model.