What is the difference between user-based and item-based collaborative filtering?

User-based and item-based collaborative filtering are two core approaches in recommendation systems, differing primarily in how they leverage data to generate suggestions. User-based filtering identifies users with similar preferences to the target user and recommends items those similar users have liked. For example, if User A and User B both enjoyed movies like Inception and The Matrix, the system might suggest Interstellar to User A if User B liked it. In contrast, item-based filtering focuses on relationships between items, recommending items similar to those the target user has already interacted with. If users who watched Inception often watch Interstellar, the system will suggest Interstellar to anyone who watched Inception, regardless of other users’ behavior.

The computational approach also differs. User-based methods calculate similarity between users, often using metrics like Pearson correlation or cosine similarity to compare user-item interaction vectors (e.g., ratings or purchase history). These similarities are then used to weight the preferences of similar users. However, user preferences can change over time, requiring frequent recalculations, which becomes computationally expensive at scale. Item-based methods, instead, precompute similarities between items, such as how often items are co-viewed or co-purchased. For instance, if 80% of users who bought a laptop also bought a mouse, the items are deemed similar. Item similarities are more stable, making this approach efficient for large datasets since precomputed item-item matrices can be reused.

Use cases often dictate which method to choose. User-based filtering works well in smaller, stable communities where user preferences are consistent, like niche forums recommending posts. Item-based excels in large, dynamic platforms like e-commerce, where item catalogs change less frequently than user behavior. For example, Netflix might use user-based for tight-knit user groups but rely on item-based for broad recommendations (e.g., “Similar to Stranger Things”). Developers should consider scalability: user-based requires real-time similarity calculations, while item-based leverages precomputed data, reducing latency. Hybrid approaches, combining both methods, are also common to balance accuracy and performance.