Seamlessly integrate multiple scoring objectives, balance personalization with business logic, and dynamically adapt models at inference time—all with native Python flexibility.
Unlock direct observability and define precise ranking logic using interpretable expressions.
Switch between personalized & trending models dynamically.
Dynamically mix personalized scores with a geo-location penalizer to connect users with nearby items.
Optimize recommendations by balancing engagement, purchase intent, and product diversity.
Dynamically decay rankings for older content while boosting high-quality journalism.
No more waiting for lengthy training cycles. Adjust ranking weights, test different scoring strategies, and refine recommendations in real-time.
Instead of using one model to optimize for all objectives, Value Modeling allows you to train separate models for clicks, purchases, or quality —then combine them dynamically.
Fine-tune recommendations, blend multiple models, and adapt in real-time—all with full transparency and no black-box limitations.