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  Collaborative Filtering via Ensembles of Matrix Factorizations

Wu, M. (2007). Collaborative Filtering via Ensembles of Matrix Factorizations. Proceedings of KDD Cup and Workshop 2007, 43-47.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CC3D-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CC3E-C
Genre: Conference Paper

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 Creators:
Wu, M1, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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 Netflixamp;lsquo;s own system.

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 Dates: 2007-08
 Publication Status: Issued
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 Identifiers: URI: http://www.cs.uic.edu/~liub/KDD-cup-2007/proceedings.html
BibTex Citekey: 4614
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Title: KDD Cup and Workshop 2007
Place of Event: San Jose, CA, USA
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Title: Proceedings of KDD Cup and Workshop 2007
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 43 - 47 Identifier: -