The Data
Recommendation systems are entirely data-driven. Data falls into two broad categories: explicit feedback and implicit feedback.
Explicit Feedback
Ratings and reviews are the classic explicit signal. A user gives an item 4 out of 5 stars. This is unambiguous: we know they interacted with it and we know how they felt. The problem is sparsity: the vast majority of users never bother to rate anything.
When was the last time you provided a review or rating for something you were recommended?
Implicit Feedback
Implicit signals are far more abundant. They include:
- Interaction events: clicks, views, purchases, playlist additions, shares, saves
- Engagement depth: viewing time/duration, scroll depth, repeat visits
- Contextual signals: time of day, day of week, seasonality, device type, location
- Session information: what was browsed before this action, what came after
Electronics
Wireless Noise-Cancelling Headphones
30-hour battery life, adaptive ANC, foldable design. Pairs instantly with up to 3 devices. Premium drivers deliver studio-quality sound across all frequencies.
Rate this item
Interact with the page to generate signals.
Interact with the simulated product page. Every action you take, clicking a tag, expanding the description, switching tabs, adding to cart, generates a signal. Watch how quickly implicit data accumulates compared to explicit feedback.
The challenge with implicit feedback is interpretation. A click might mean enthusiasm, curiosity, or an accidental tap. A long watch time might mean love... or that the user fell asleep. Careful signal engineering is required to construct meaningful training targets.
Item and User Metadata
Beyond interactions, recommenders can leverage content metadata: genre, tags, category, release date, popularity metrics, and item descriptions. On the user side: demographics (age, location), account information, and subscription tier. These become especially valuable for addressing the cold-start problem.
A user browses a product page for 3 minutes but doesn't buy it. How might a recommendation system interpret this signal, and what ambiguities exist?