Inferring Searcher Attention by Jointly Modeling User Interactions and Content Salience

Abstract

Modeling and predicting user attention is crucial for interpreting search behavior. The numerous applications include quantifying web search satisfaction, estimating search quality, and measuring and predicting online user engagement. While prior research has demonstrated the value of mouse cursor data and other interactions as a rough proxy of user attention, precisely predicting where a user is looking on a page remains a challenge, exacerbated in Web pages beyond the traditional search results. To improve attention prediction on a wider variety of Web pages, we propose a new way of modeling searcher behavior data by connecting the user interactions to the underlying Web page content. Specifically, we propose a principled model for predicting a searcher's gaze position on a page, that we call Mixture of Interactions and Content Salience (MICS). To our knowledge, our model is the first to effectively combine user interaction data, such as mouse cursor and scrolling positions, with the visual prominence, or salience, of the page content elements. Extensive experiments on multiple popular types of Web content demonstrate that the proposed MICS model significantly outperforms previous approaches to searcher gaze prediction that use only the interaction information. Grounding the observed interactions to the underlying page content provides a general and robust approach to user attention modeling, enabling more powerful tool for search behavior interpretation and ultimately search quality improvements.