Personalized click prediction in sponsored search

Sponsored search is a multi-billion dollar business that gen-erates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to sponsored search because the prediction is used to influence ranking, filtering, placement, and pricing of ads. Ad ranking, fil-tering and placement have a direct impact on the user ex-perience, as users expect the most useful ads to rank high and be placed in a prominent position on the page. Pric-ing impacts the advertisers ’ return on their investment and revenue for the search engine. The objective of this paper is to present a framework for the personalization of click models in sponsored search. We develop user-specific and demographic-based features that reflect the click behavior of individuals and groups. The features are based on obser-vations of search and click behaviors of a large number of users of a commercial search engine. We add these features to a baseline non-personalized click model and perform ex-periments on offline test sets derived from user logs as well as on live traffic. Our results demonstrate that the per-sonalized models significantly improve the accuracy of click prediction