Applications of the conjugate gradient method for implicit feedback collaborative filtering

Gábor Takács, István Pilászy, Domonkos Tikk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models.

Original languageEnglish
Title of host publicationRecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems
Pages297-300
Number of pages4
DOIs
Publication statusPublished - Dec 6 2011
Event5th ACM Conference on Recommender Systems, RecSys 2011 - Chicago, IL, United States
Duration: Oct 23 2011Oct 27 2011

Publication series

NameRecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

Other

Other5th ACM Conference on Recommender Systems, RecSys 2011
CountryUnited States
CityChicago, IL
Period10/23/1110/27/11

Keywords

  • collaborative filtering
  • conjugate gradient method

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems

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  • Cite this

    Takács, G., Pilászy, I., & Tikk, D. (2011). Applications of the conjugate gradient method for implicit feedback collaborative filtering. In RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems (pp. 297-300). (RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems). https://doi.org/10.1145/2043932.2043987