Scalable collaborative filtering approaches for large reeommender systems

Gábor Takáes, István Pilászy, Bottyán Németh, Domonkos Tikk

Research output: Contribution to journalArticle

299 Citations (Scopus)


The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.

Original languageEnglish
Pages (from-to)623-656
Number of pages34
JournalJournal of Machine Learning Research
Publication statusPublished - Jan 1 2009


  • Collaborative filtering
  • Matrix factorization
  • Neighbor based correction
  • Netflix Prize
  • Reeommender systems

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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