Collaborative Filtering
Collaborative filtering is the foundation of modern recommendation systems. By analyzing patterns in user behavior, these algorithms discover that users who bought X also tend to buy Y, enabling powerful "you might also like" recommendations.
Approaches Compared
| Method | How It Works | Pros | Cons |
|---|---|---|---|
| User-Based CF | Find similar users, recommend their items | Intuitive, captures taste clusters | Does not scale well, sparse data |
| Item-Based CF | Find similar items to what user liked | Scales better, stable similarities | Less serendipitous discovery |
| Matrix Factorization | Decompose user-item matrix into latent factors | Handles sparsity, good accuracy | Batch training, no new user handling |
| ALS | Alternating Least Squares for implicit feedback | Handles views/clicks (no ratings) | Needs tuning for implicit signals |
Implicit vs. Explicit Feedback
Explicit Feedback
Ratings, reviews, and likes. High signal quality but extremely sparse in e-commerce. Most users never rate products they purchase.
Implicit Feedback
Views, clicks, cart adds, purchases, and time spent. Abundant but noisy. A view does not mean interest; a non-view does not mean disinterest.
Weighted Signals
Assign different confidence weights: purchase > cart add > wishlist > click > view. This captures the varying strength of implicit signals.
Recency Weighting
Recent interactions carry more signal than old ones. Apply exponential decay to older events to keep recommendations fresh and relevant.
Implementation Steps
- Collect Interaction Data: Log all user-product interactions with timestamps, event types, and session context
- Build Interaction Matrix: Create a sparse user-item matrix with weighted implicit feedback scores
- Train Model: Use ALS (for implicit) or SVD (for explicit) to learn latent factor representations
- Generate Candidates: For each user, find products with the highest predicted scores from the trained model
- Update Regularly: Retrain daily or weekly to incorporate new interactions and product catalog changes
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