Utilizing a Geometry of Context For Enhanced Implicit Feedback

Implicit feedback algorithms utilize interaction between
searchers and search systems to learn more about users’
needs and interests than expressed in query statements
alone. This additional information can be used to formulate
improved queries or directly improve retrieval performance.
In this paper we present a geometric framework
that utilizes multiple sources of evidence present in this interaction
context (e.g., display time, document retention)
to develop enhanced implicit feedback models personalized
for each user and tailored for each search task. We use rich
interaction logs (and associated metadata such as relevance
judgments), gathered during a longitudinal user study, as
relevance stimuli to compare an implicit feedback algorithm
developed using the framework with alternative algorithms.
Our findings demonstrate both the effectiveness of our proposed
algorithm and the potential value of incorporating
multiple sources of interaction evidence when developing implicit
feedback algorithms.