Work

Collaborative Filtering via Ensembles of Matrix Factorizations

Year: 2007

Type: conference-paper

Abstract: We present a Matrix Factorization(MF) based approach for the Netflix Prize competition. Currently MF based algorithms are popular and have proved successful for collaborative filtering tasks. For the Netflix Prize competition, we adopt three different types of MF algorithms: regularized MF, maximum margin MF and non-negative MF. Furthermore, for each MF algorithm, instead of selecting the optimal parameters, we combine the results obtained with several parameters. With this method, we achieve a performance that is more than 6 better than the Netflix's own system.

Source: Max Planck Institute for Plasma Physics

Author: Mingrui Wu

Institutions: Max Planck Society, Max Planck Institute for Biological Cybernetics

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W1575641478

FWCI: 2.482

Cites: 20

Cited by: 82

Related to: 10