Detecting sponsored recommendations

Subhashini Krishnasamy, Rajat Sen, Sanjay Shakkottai, Sewoong Oh

Research output: Contribution to journalConference article

Abstract

Personalized recommender systems provide great opportunities for targeted advertisements, by displaying ads alongside genuine recommendations. We consider a biased recommendation system where such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to users. We consider the problem of detecting such a bias and propose an algorithm that uses statistical analysis based on binary feedback data from a subset of users. We prove that the proposed algorithm detects bias with high probability for a broad class of recommendation systems with sufficient number of feedback samples.

Original languageEnglish (US)
Pages (from-to)445-446
Number of pages2
JournalPerformance Evaluation Review
Volume43
Issue number1
DOIs
StatePublished - Jun 24 2015
EventACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2015 - Portland, United States
Duration: Jun 15 2015Jun 19 2015

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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  • Cite this

    Krishnasamy, S., Sen, R., Shakkottai, S., & Oh, S. (2015). Detecting sponsored recommendations. Performance Evaluation Review, 43(1), 445-446. https://doi.org/10.1145/2796314.2745885