  Natural interaction with recommendation and personalized search systems has
received tremendous attention in recent years. We focus on the challenge of
supporting people's understanding and control of these systems and explore a
fundamentally new way of thinking about representation of knowledge in
recommendation and personalization systems. Specifically, we argue that it may
be both desirable and possible for algorithms that use natural language
representations of users' preferences to be developed. We make the case that
this could provide significantly greater transparency, as well as affordances
for practical actionable interrogation of, and control over, recommendations.
Moreover, we argue that such an approach, if successfully applied, may enable a
major step towards systems that rely less on noisy implicit observations while
increasing portability of knowledge of one's interests.
