Beyond Passive Preference Inference: User-Governed Preference Memory and Calibrated Initiative in Recommender Systems
Recommender systems remain organized around passive preference inference: estimate latent interest from behavioral traces, then rank. Past: implicit feedback, matrix factorization, learning-to-rank, and large-scale offline evaluation made this abstraction highly productive. Present: four mismatches expose what predicti...