Handling Advertisements of Unknown Quality in Search Advertising

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

Bibtex Metadata Paper


Sandeep Pandey, Christopher Olston


We consider how a search engine should select advertisements to display with search results, in order to maximize its revenue. Under the standard "pay-per-click" arrangement, revenue depends on how well the displayed advertisements appeal to users. The main difficulty stems from new advertisements whose degree of appeal has yet to be determined. Often the only reliable way of determining appeal is exploration via display to users, which detracts from exploitation of other advertisements known to have high appeal. Budget constraints and finite advertisement lifetimes make it necessary to explore as well as exploit. In this paper we study the tradeoff between exploration and exploitation, modeling advertisement placement as a multi-armed bandit problem. We extend traditional bandit formulations to account for budget constraints that occur in search engine advertising markets, and derive theoretical bounds on the performance of a family of algorithms. We measure empirical performance via extensive experiments over real-world data.