  We address how to robustly interpret natural language refinements (or
critiques) in recommender systems. In particular, in human-human recommendation
settings people frequently use soft attributes to express preferences about
items, including concepts like the originality of a movie plot, the noisiness
of a venue, or the complexity of a recipe. While binary tagging is extensively
studied in the context of recommender systems, soft attributes often involve
subjective and contextual aspects, which cannot be captured reliably in this
way, nor be represented as objective binary truth in a knowledge base. This
also adds important considerations when measuring soft attribute ranking. We
propose a more natural representation as personalized relative statements,
rather than as absolute item properties. We present novel data collection
techniques and evaluation approaches, and a new public dataset. We also propose
a set of scoring approaches, from unsupervised to weakly supervised to fully
supervised, as a step towards interpreting and acting upon soft attribute based
critiques.
