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ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems
2017
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Matrix approximation (MA) is one of the most popular techniques in today's recommender systems. In most MA-based recommender systems, the problem of risk minimization should be defined, and how to achieve minimum expected risk in model learning is one of the most critical problems to recommendation accuracy. This paper addresses the expected risk minimization problem, in which expected risk can be bounded by the sum of optimization error and generalization error. Based on the uniform stability
doi:10.1609/aaai.v31i1.10743
fatcat:e665ljx7unberiti6tnzrahql4