A Preference Approach to Reputation in Sponsored Search

Aritra Ghosh, Dinesh Gaurav, Rahul Agrawal

Abstract: Determining reputation of an advertiser in sponsored search is a recent important problem with direct impact on revenue for web publishers and relevance of ads. Individual performance of advertisers is usually expressed through observed click through rate, which depends on advertiser reputation, ad relevance and position. However, advertiser reputation has not been explicitly modeled in click prediction literature. Using traditional approaches in web page popularity for organic search in this context is not reasonable as the notion of link-structure in web is not directly applicable to sponsored search. In this study, we motivate and propose a pairwise preference relation model to study the advertiser reputation problem. Pairwise comparisons of advertisers give information over and above the information available in their individual historical performances. We relate the notion of preference among the advertisers to the spectral properties of the preference graph. We provide empirical evidence of the existence of reputation bias in click behavior. Consequently, we experiment with this signal to improve click prediction.