Improving searcher models using mouse cursor activity

Web search components such as ranking and query suggestions analyze the user data provided in query and click logs. While this data is easy to collect and provides information about user behavior, it omits user interactions with the search engine that do not hit the server; these logs omit search data such as users' cursor movements. Just as clicks provide signals for relevance in search results, cursor hovering and scrolling can be additional implicit signals. In this work, we demonstrate a technique to extend models of the user's search result examination state to infer document relevance. We start by exploring recorded user interactions with the search results, both qualitatively and quantitatively. We find that cursor hovering and scrolling are signals telling us which search results were examined, and we use these interactions to reveal latent variables in searcher models to more accurately compute document attractiveness and satisfaction. Accuracy is evaluated by computing how well our model using these parameters can predict future clicks for a particular query. We are able to improve the click predictions compared to a basic searcher model for higher ranked search results using the additional log data.