Estimating Credibility of User Clicks with Mouse Movement and Eye-Tracking Information

Click-through information has been regarded as one of the most important signals for implicit relevance feedback in Web search engines. Because large variation exists in users’ personal characteristics, such as search expertise, domain knowledge, and carefulness, different user clicks should not be treated as equally important. Different from most existing works that try to estimate the credibility of user clicks based on click-through or querying behavior, we propose to enrich the credibility estimation framework with mouse movement and eye-tracking information. In the proposed framework, the credibility of user clicks is evaluated with a number of metrics in which a user in the context of a certain search session is treated as a relevant document classifier. With an experimental search engine system that collects click-through, mouse movement, and eye movement data simultaneously, we find that credible user behaviors could be separated from non-credible ones with a number of interaction behavior features. Further experimental results indicate that relevance prediction performance could be improved with the proposed estimation framework.