A Preference Approach to Reputation in Sponsored Search

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 rates (CTR), 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 define the notion of preference among the advertisers and relate the advertiser reputation problem 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.