Query Augmentation based Intent Matching in Retail Vertical Ads

Search advertising shows trends of vertical extension. Vertical ads, including product ads and local search ads, are proliferating at an ever increasing pace. They typically offer better ROI to advertisers as a result of better user engagement. However, campaigns 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 an ad retrieval framework for retail vertical ads, based on query rewrite using personal history data to improve ad recall rate. To insure ad quality, we also present a relevance model for matching rewritten queries with user search intent, with a particular focus on tail queries. In addition, we designed and implemented a GPU-based system to accelerate the training of the relevance model to meet production performance constraints. Finally, we carry out extensive experiments on large-scale logs collected from Bing, and show significant gains in ad retrieval rate without compromising ad quality.