Query augmentation based intent matching in retail vertical ads

Huasha Zhao, Wei Vivian Zhang, Ye Chen, John F. Canny, Tak W. Yan

Abstract: Search advertising shows trends of vertical extension. Vertical ads typically offer better Return of Investment (ROI) to advertisers as a result of better user engagement. However, campaign and bids in vertical ads are not set at the keyword level. As a result, the matching between user query and ads suffers low recall rate and the match quality is heavily impacted by tail queries. In this paper, we propose a retail ads retrieval framework based on query rewrite using personal history data to improve ads recall rate. To insure ads quality, we also present a relevance model for matching rewritten queries with user search intent, with a particular focus on rare queries. Extensive experiments are carried out on large-scale logs collected from the Bing search engine, and results show our system achieves significant gains in ads retrieval rate without compromising ads quality. To our knowledge, this work is the first attempt to leverage user behavioral data in ad matching and apply it to the vertical ads domain.