Cleaning up toxic waste: Removing nefarious contributions to recommendation systems

Adam Charles, Ali Ahmed, Aditya Joshi, Stephen Conover, Christopher Turnes, Mark Davenport

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

Abstract

Recommendation systems are becoming increasingly important, as evidenced by the popularity of the Netflix prize and the sophistication of various online shopping systems. With this increase in interest, a new problem of nefarious or false rankings that compromise a recommendation system's integrity has surfaced. We consider such purposefully erroneous rankings to be a form of 'toxic waste,' corrupting the performance of the underlying algorithm. In this paper, we propose an adaptive reweighted algorithm as a possible approach towards correcting this problem. Our algorithm relies on finding a low-rank-plus-sparse decomposition of the recommendation matrix, where the adaptation of the weights aids in rejecting the malicious contributions. Simulations suggest that our algorithm converges fairly rapidly and produces accurate results.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages6571-6575
Number of pages5
DOIs
StatePublished - Oct 18 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Adaptive optimization
  • convergence
  • sparsity
  • toxic waste

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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