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 language | English (US) |
|---|---|
| Pages (from-to) | 445-446 |
| Number of pages | 2 |
| Journal | Performance Evaluation Review |
| Volume | 43 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jun 24 2015 |
| Event | ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2015 - Portland, United States Duration: Jun 15 2015 → Jun 19 2015 |
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Networks and Communications