Personalized Recommendations at Scale
Deliver personalized product, content, or feature recommendations to users by leveraging user behavior and item data — improving engagement and conversions while keeping implementation details private.
1
Collect User & Item Data
Gather interaction events, preferences, and item metadata to form the recommendation dataset.
2
Generate Candidate Suggestions
The system produces a set of relevant candidate items tailored to each user profile.
3
Rank & Personalize
Candidates are ranked by relevance and business objectives to surface the best recommendations.
4
Serve & Monitor
Recommendations are delivered via API or UI and monitored for performance to iteratively improve relevance.