predictive-personalization

Deliver personalized product recommendations by collecting behavioral events, maintaining Redis-backed user profiles, and serving widgets via headless storefronts. This approach ranks candidates using recent views, PDP context, and category affinity to increase relevance and conversion.

ML personalization handles cold-start users by falling back to popular items when behavioral data is insufficient. It also uses precomputed item-item similarities and category affinity from available signals to generate relevant recommendations for new visitors.

Yes, you can use Redis-backed user profiles to personalize email blocks in platforms like Klaviyo. The system predicts customer preferences and embeds personalized product recommendations directly within email flows to lift CTR and attributed revenue.

The best way to A/B test personalized product recommendations is using deterministic MD5 assignment. This ensures consistent experiment grouping across users while evaluating different recommendation strategies, such as varying category diversity or serendipity slots.

Prevent recommendation carousels from showing too many similar items by enforcing per-category limits and reserving serendipity slots. This ensures category diversity across the displayed products, improving the user experience and relevance.

Yes, precomputed item-item similarities are needed for predictive personalization to rank candidates effectively. These similarities are rebuilt nightly, combining with recent-views and PDP context to surface accurate product recommendations.