# Mittagsseminar (in cooperation with A. Steger, D. Steurer and B. Sudakov)

__Mittagsseminar Talk Information__ | |

**Date and Time**: Tuesday, October 06, 2009, 12:15 pm

**Duration**: This information is not available in the database

**Location**: OAT S15/S16/S17

**Speaker**: Martin Jaggi

## Approximate SDP solvers, Matrix Factorizations, the Netflix Prize, and PageRank

We will present a new simple approximation algorithm of Hazan [Hazan
LATIN '08] to solve arbitrary semidefinite programs (SDPs).

Furthermore, we will discuss matrix factorization techniques in
machine learning. The task here is that given just a few of the
entries of a large real matrix, we try to predict the unknown entries
by building a simple factor model of the matrix. Here 'simple' either
means low rank or low norm. Such matrix factorization techniques are
at the core of current recommender systems as in the recently ended
Netflix Prize competition [Short
IEEE article,
Wikipedia:
Netflix Prize].

In the last part we will apply Hazan's approximate SDP solver to
solve matrix factorization problems, and observe that its performance
is comparable to the best existing algorithms. The performance is
limited by how fast we can find the principal eigenvector of the
adjacency matrix of a weighted bipartite graph.

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