Content-Based Recommendation

Overview

Content-based filtering recommends items whose features match the user's preference profile, built from their interaction history. Computes in O(I × F) per user where I=items, F=features. Solves new-item cold start since items only need features, not interaction history.

When to Use

Trigger conditions:

• Recommending based on item attributes (genre, category, keywords, price range)
• New item cold start: items have features but no interaction data yet
• When user privacy requires no cross-user data sharing

When NOT to use:

• When serendipity matters (content-based creates filter bubbles)
• When item features are unavailable or uninformative (use CF instead)

Algorithm