Collaborative Filtering (CF) is a recommendation technique that predicts a user’s preference for an item based on similarities between users or items. It assumes that users with similar tastes in the past will have similar tastes in the future.
How It Works
- Collect user–item interaction data (ratings, clicks, purchases).
- Predict unknown preferences by leveraging patterns from known interactions.
Types of Collaborative Filtering
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User-Based CF
- Finds users similar to the target user and recommends items they liked.
- Example: “Users like you also enjoyed…”
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Item-Based CF
- Finds items similar to those the user liked and recommends them.
- Example: “Because you watched X, you might like Y.”