When Choice Happens: A Systematic Examination of Mouse Movement Length for Decision Making in Web Search

Abstract

Searchers often make a choice in a matter of seconds on SERPs. As a result of a dynamic cognitive process, choice is ultimately reflected in motor movement and thus can be modeled by tracking the computer mouse. However, because not all movements have equal value, it is important to understand how do they and, critically, their sequence length impact model performance. We study three different SERP scenarios where searchers (1)noticed an advertisement, (2)abandoned the page, and (3)became frustrated. We model these scenarios with recurrent neural nets and study the effect of mouse sequence padding and truncating to different lengths. We find that it is possible to predict the aforementioned tasks sometimes using just 2 seconds of movement. Ultimately, by efficiently recording the right amount of data, we can save valuable bandwidth and storage, respect the users' privacy, and increase the speed at which machine learning models can be trained and deployed. Considering the web scale, doing so will have a net benefit on our environment.