Charlie Hubbard from Dr. Hegde’s research group will be giving a talk on “Parallel Methods for Matrix Completion”. Please note the venue, it is NOT our usual place.
Date: April 4th, 2017
Time: 3:00 – 4:00 pm
Venue: 3043 Coover hall
Charlie’s abstract: As a graduate student, you don’t have time to search through the entire Netflix library for a movie you’ll like…you barely have time to watch a movie in the first place! Thankfully, Netfilx excels at content recommendation, it is able to present you with twenty or so movies from its entire library that it knows you’ll enjoy watching (while you do homework). In recent years it has been shown that matrix completion can be a useful tool for content recommendation: given a sparse matrix of users-item ratings, matrix completion can be used to predict the unseen ratings. The problem for large-scale content providers, like Amazon and Netflix, is that the size of their user-item matrices (easily 100,000 x 10,000) make most matrix completion approaches infeasible. In this talk I will discuss: two scalable methods (Jellyfish and Hogwild!) for parallel matrix completion, a GPU-based implementation of Jellyfish and preliminary results from an unnamed algorithm for parallel inductive matrix completion.