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Activity Number: 454 - Recommender Systems and Large-Margin Machines: From Statistics Perspectives
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #307325 Presentation
Title: Two Improvements to the Matrix Factorization Approach for Recommender Systems
Author(s): Mu Zhu*
Companies: University of Waterloo
Keywords: alternating optimization; coordinate descent; saddle points

I describe two improvements to the matrix factorization (MF) approach for making personalized recommendations, popularized by the 2006-2009 Netflix Contest. First, content information can be incorporated directly into the MF approach. This not only improves recommendation accuracy, but also provides useful insights about the contents themselves, as well as makes the resulting recommendations more easily interpretable. Second, the underlying optimization problem driving the MF approach is nonconvex, with many traps in practice --- such as inferior local solutions and saddle points, but the search space of the optimization algorithm can be expanded in order to avoid some of them. (Based on joint works with Peter Forbes, Jennifer Nguyen, and W. James Murdoch.)

Authors who are presenting talks have a * after their name.

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