Calibrating Recommendations to Better Match User Interests
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Re: Calibrating Recommendations to Better Match User Interests
#2Recommender 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.
Re: Calibrating Recommendations to Better Match User Interests
#3How 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?