Convergence analysis for an online recommendation system

Anh Truong, Negar Kiyavash, Vivek Borkar

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

Abstract

Online recommendation systems use votes from experts or other users to recommend objects to customers. We propose a recommendation algorithm that uses an average weight updating rule and prove its convergence to the best expert and derive an upper bound on its loss. Often times, recommendation algorithms make assumptions that do not hold in practice such as requiring a large number of the good objects, presence of experts with the exact same taste as the user receiving the recommendation, or experts who vote on all or majority of objects. Our algorithm relaxes these assumptions. Besides theoretical performance guarantees, our simulation results show that the proposed algorithm outperforms current state-of-the-art recommendation algorithm, Dsybil.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Pages3889-3894
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
CountryUnited States
CityOrlando, FL
Period12/12/1112/15/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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