Adaptive modeling for large-scale advertisers optimization

Abstract

Abstract Background Advertisers optimization is one of the most fundamental tasks in paid search, which is a multi-billion industry as a major part of the growing online advertising market. As paid search is a three-player game (advertisers, search users and publishers), how to optimize large-scale advertisers to achieve their expected performance becomes a new challenge, for which adaptive models have been widely used. Main body In this paper, we provide a review of the recent progress on adaptive modeling for the task of large-scale advertisers optimization in paid search, including keyword recommendation which can automatically suggest the relevant and competitive keywords to match queries of users input, bid suggestion which can efficiently give rational bids to help win the participated auctions and budget optimization which helps the advertisers show ads throughout the whole period. In addition, some related practical tools of advertiser optimization are introduced. Conclusion Finally, we conclude that it has attracted much attention on large-scale advertisers optimization in both industry and research community and has achieved tremendous advance over the recent decade, especially for the adaptive models. Moreover, we discuss possible directions of future research on advertisers optimization.