  This paper introduces Seeker, a system that allows users to interactively
refine search rankings in real time, through feedback in the form of likes and
dislikes. When searching online, users may not know how to accurately describe
their product of choice in words. An alternative approach is to search an
embedding space, allowing the user to query using a representation of the item
(like a tune for a song, or a picture for an object). However, this approach
requires the user to possess an example representation of their desired item.
Additionally, most current search systems do not allow the user to dynamically
adapt the results with further feedback. On the other hand, users often have a
mental picture of the desired item and are able to answer ordinal questions of
the form: "Is this item similar to what you have in mind?" With this
assumption, our algorithm allows for users to provide sequential feedback on
search results to adapt the search feed. We show that our proposed approach
works well both qualitatively and quantitatively. Unlike most previous
representation-based search systems, we can quantify the quality of our
algorithm by evaluating humans-in-the-loop experiments.
