Collaborative Filtering

Overview

Collaborative filtering recommends items based on collective user behavior patterns. User-based CF finds similar users; item-based CF finds similar items. Computes in O(U² × I) for user-based or O(I² × U) for item-based where U=users, I=items.

When to Use

Trigger conditions:

• Building recommendations from user-item interaction data (ratings, clicks, purchases)
• Finding "users like you also liked" or "frequently bought together" patterns

When NOT to use:

• When you have no interaction data (cold start — use content-based filtering)
• When item features matter more than behavior patterns (use content-based)

Algorithm