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Collaborative Filtering via Ensembles of Matrix Factorizations

2007

Conference Paper

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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.

Author(s): Wu, M.
Book Title: KDD Cup and Workshop 2007
Journal: Proceedings of KDD Cup and Workshop 2007
Pages: 43-47
Year: 2007
Month: August
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: KDD Cup and Workshop 2007
Event Place: San Jose, CA, USA

Digital: 0
Institution: ACM Special Interest Group on Knowledge Discovery and Data Mining
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4614,
  title = {Collaborative Filtering via Ensembles of Matrix Factorizations},
  author = {Wu, M.},
  journal = {Proceedings of KDD Cup and Workshop 2007},
  booktitle = {KDD Cup and Workshop 2007},
  pages = {43-47},
  organization = {Max-Planck-Gesellschaft},
  institution = {ACM Special Interest Group on Knowledge Discovery and Data Mining},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2007},
  month_numeric = {8}
}