Detecting Sponsored Recommendations

ACM Trans. Model. Perform. Eval. Comput. Syst.

Shakkottai

et al. 2016

Self Cite

With the vast number of items, Web pages, and news from which to choose, online services and customers both benefit tremendously from personalized recommender systems. Such systems additionally provide great opportunities for targeted advertisements by displaying ads alongside genuine recommendations. We consider a biased recommendation system in which such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to a single user. We ask whether it is possible for a small subset of collaborating users to detect such bias. We propose an algorithm that can detect this type of bias through statistical analysis on the collaborating users' feedback. The algorithm requires only binary information indicating whether a user was satisfied with each of the recommended item or not. This makes the algorithm widely appealing to real-world issues such as identification of search engine bias and pharmaceutical lobbying. We prove that the proposed algorithm detects the bias with high probability for a broad class of recommendation systems when a sufficient number of users provides feedback on a sufficient number of recommendations. We provide extensive simulations with real datasets and practical recommender systems, which confirm the trade-offs in the theoretical guarantees.

Section: Algorithmmentioning

confidence: 99%

Detecting Sponsored Recommendations

ACM Trans. Model. Perform. Eval. Comput. Syst.

Shakkottai

et al. 2016

Self Cite

Detecting Sponsored Recommendations

Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

et al. 2015

Self Cite

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.

Detecting Sponsored Recommendations

SIGMETRICS Perform. Eval. Rev.

et al. 2015

Self Cite

With a vast number of items, web-pages, and news to choose from, online services and the customers both benefit tremendously from personalized recommender systems. Such systems however 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 a single user. We ask whether it is possible for a small subset of collaborating users to detect such a bias. We propose an algorithm that can detect such a bias through statistical analysis on the collaborating users' feedback. The algorithm requires only binary information indicating whether a user was satisfied with each of the recommended item or not. This makes the algorithm widely appealing to real world issues such as identification of search engine bias and pharmaceutical lobbying. We prove that the proposed algorithm detects the bias with high probability for a broad class of recommendation systems when sufficient number of users provide feedback on sufficient number of recommendations. We provide extensive simulations with real data sets and practical recommender systems, which confirm the trade offs in the theoretical guarantees.