Large-Scale Analysis of Viewing Behavior: Towards Measuring Satisfaction with Mobile Proactive Systems
Abstract
Recently, proactive systems such as Google Now and Microsoft Cortana have become
increasingly popular in reforming the way users access information on mobile
devices. In these systems, relevant content is presented to users based on their
context without a query in the form of information cards that do not require a
click to satisfy the users. As a result, prior approaches based on clicks cannot
provide reliable measurements of user satisfaction with such systems. It is also
unclear how much of the previous findings regarding good abandonment with reactive
Web searches can be applied to these proactive systems due to the intrinsic
difference in user intent, the greater variety of content types and their
presentations. In this paper, we present the first large-scale analysis of viewing
behavior based on the viewport (the visible fraction of a Web page) of the mobile
devices, towards measuring user satisfaction with the information cards of the
mobile proactive systems. In particular, we identified and analyzed a variety of
factors that may influence the viewing behavior, including biases from ranking
positions, the types and attributes of the information cards, and the touch
interactions with the mobile devices. We show that by modeling the various factors
we can better measure user satisfaction with the mobile proactive systems, enabling
stronger statistical power in large-scale online A/B testing.
