Fast query evaluation for ad retrieval

Ye Chen, Mitali Gupta, Tak W. Yan

Abstract: We describe a fast query evaluation method for ad document retrieval in online advertising, based upon the classic WAND algorithm. The key idea is to localize per-topic term upper bounds into homogeneous ad groups. Our approach is not only theoretically motivated by a topical mixture model; but empirically justified by the characteristics of the ad domain, that is, short and semantically focused documents with natural hierarchy. We report experimental results using artificial and real-world query-ad retrieval data, and show that the tighter-bound WAND outperforms the traditional approach by 35.4% reduction in number of full evaluations.