A Harmonic Extension Approach for Collaborative Ranking

16 Feb 2016Da KuangZuoqiang ShiStanley OsherAndrea Bertozzi

We present a new perspective on graph-based methods for collaborative ranking for recommender systems. Unlike user-based or item-based methods that compute a weighted average of ratings given by the nearest neighbors, or low-rank approximation methods using convex optimization and the nuclear norm, we formulate matrix completion as a series of semi-supervised learning problems, and propagate the known ratings to the missing ones on the user-user or item-item graph globally... (read more)

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