  Many information access systems operationalize their results in terms of
rankings, which are then displayed to users in various ranking layouts such as
linear lists or grids. User interaction with a retrieved item is highly
dependent on the item's position in the layout, and users do not provide
similar attention to every position in ranking (under any layout model). User
attention is an important component in the evaluation process of ranking, due
to its use in effectiveness metrics that estimate utility as well as fairness
metrics that evaluate ranking based on social and ethical concerns. These
metrics take user browsing behavior into account in their measurement
strategies to estimate the attention the user is likely to provide to each item
in ranking. Research on understanding user browsing behavior has proposed
several user browsing models, and further observed that user browsing behavior
differs with different ranking layouts. However, the underlying concepts of
these browsing models are often similar, including varying components and
parameter settings. We seek to leverage that similarity to represent multiple
browsing models in a generalized, configurable framework which can be further
extended to more complex ranking scenarios. In this paper, we describe a
probabilistic user browsing model for linear rankings, show how they can be
configured to yield models commonly used in current evaluation practice, and
generalize this model to also account for browsing behaviors in grid-based
layouts. This model provides configurable framework for estimating the
attention that results from user browsing activity for a range of IR evaluation
and measurement applications in multiple formats, and also identifies
parameters that need to be estimated through user studies to provide realistic
evaluation beyond ranked lists.
