Calibrating Recommendations to Better Match User Interests

Recommender systems often overfocus on dominant interests, neglecting diversity. Shaped introduces a method to calibrate recommendations using minimum-cost flow optimization, ensuring results reflect the breadth of user preferences. This approach improves balance and relevance, outperforming standard methods.

How do you define and quantify ‘calibration’ in this context? Is it purely based on aligning recommendations with explicit user preferences, or are you also trying to infer latent interests?