NYU & UBC Propose Deviation-Based Learning to Advance Recommender System Training | Synced
A research team from New York University and the University of British Columbia proposes deviation-based learning, a novel approach for training recommender systems that learns user knowledge by ob...
Source: Synced | AI Technology & Industry Review
A research team from New York University and the University of British Columbia proposes deviation-based learning, a novel approach for training recommender systems that learns user knowledge by observing whether they follow or deviate from recommendations.