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Recommendation Systems • Collaborative Filtering (Matrix Factorization)Hard⏱️ ~3 min

Implicit vs Explicit Feedback

Key Distinction
Explicit feedback means users tell you their preferences directly: star ratings, thumbs up/down, reviews. Implicit feedback means you infer preferences from behavior: clicks, purchases, watch time, scroll depth. Most real systems have abundant implicit data but sparse explicit data.
The Implicit Feedback Challenge
Implicit feedback only tells you what users did, not what they avoided. A missing interaction could mean: user has not seen the item, user saw it and was not interested, or user is interested but has not acted yet. You cannot distinguish these cases. This means you cannot treat missing interactions as negative examples.
With explicit ratings, missing means unknown. With implicit, missing could be negative or unknown. Training methods must handle this asymmetry. Common approaches: weight observed interactions higher, sample negatives from unobserved pairs, or use specialized loss functions like Bayesian Personalized Ranking (BPR).
Confidence Weighting
Not all implicit signals have equal strength. A purchase signals stronger preference than a click. Watching 90% of a video signals more than watching 10%. Weight training examples by confidence. A common formula: confidence = 1 + alpha * (interaction_count). More interactions with an item means higher confidence in the preference.
⚠️ Interview Question: "How do you handle implicit feedback?" Explain the asymmetry problem first, then mention solutions: confidence weighting, negative sampling strategies, and ranking losses like BPR that optimize relative preferences rather than absolute predictions.

💡 Key Takeaways

✓Explicit feedback: ratings, likes, thumbs up. Clear signal but rare: <1% of purchases get rated, 5-10% of watched content gets rated

✓Implicit feedback: clicks, views, purchases, time spent. Abundant (1000x more data) but ambiguous (click without purchase = dislike or distraction?)

✓Key ambiguity: no interaction could mean not interested OR never saw it. Cannot distinguish without tracking exposure

✓Interpretation: 20 min watch time good or bad? Depends if video is 25 min or 2 hours. Must normalize by context

✓Handling implicit in CF: treat as confidence weights. View = weak positive (confidence 1), purchase = strong positive (confidence 10), non-interaction = weak negative (0.1)

✓Production systems use implicit feedback because it is 100-1000x more abundant. Challenge is interpretation, not collection

📌 Interview Tips

1For system design: draw the two-stage pipeline - MF retrieves 500-2000 candidates via ANN search (5-15ms), then a ranker with richer features scores and sorts (10-30ms).

2When asked about online updates: explain that user vectors can be updated in real-time by re-solving least squares with new interactions, while item vectors are batch-updated.

3For latency discussion: break down the budget - user vector lookup/computation (1-3ms), ANN search over item embeddings (3-10ms), network overhead (2-5ms).

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