Improving Bounce Rate Prediction for Rare Queries by Leveraging Landing Page Signals

Abstract

Bounce rate prediction for clicked ads in sponsored search advertising is crucial for improving the quality of ads shown to the user. Bounce rate represents the proportion of landing pages for clicked ads on which users spend less than a specified time signifying that the user did not find a possible match of their query intent with the landing page content. In the pay-per-click revenue model for search engines, higher bounce rates mean advertisers get charged without meaningful user engagement, which impacts user and advertiser retention in long term. In real-time search engine settings complex ML models are prohibitive due to stringent latency requirements. Also historical logs are ineffective for rare queries (tail) where the data is sparse, as well as for matching user intent to adcopy when the query and bidded keywords don’t exactly overlap (smart match). In this paper, we propose a real-time bounce rate prediction system that leverages lightweight features like modified tf, positional and proximity features computed from ad landing pages and improves prediction for rare queries. The model preserves privacy and uses no user based feature. The entire ensemble is trained on millions of examples from the offline user log of the Bing commercial search engine and improves the ranking metrics for tail queries and smart match by more than 2x compared to a model that only uses ad-copy-advertiser features.