  Conversations aimed at determining good recommendations are iterative in
nature. People often express their preferences in terms of a critique of the
current recommendation (e.g., "It doesn't look good for a date"), requiring
some degree of common sense for a preference to be inferred. In this work, we
present a method for transforming a user critique into a positive preference
(e.g., "I prefer more romantic") in order to retrieve reviews pertaining to
potentially better recommendations (e.g., "Perfect for a romantic dinner"). We
leverage a large neural language model (LM) in a few-shot setting to perform
critique-to-preference transformation, and we test two methods for retrieving
recommendations: one that matches embeddings, and another that fine-tunes an LM
for the task. We instantiate this approach in the restaurant domain and
evaluate it using a new dataset of restaurant critiques. In an ablation study,
we show that utilizing critique-to-preference transformation improves
recommendations, and that there are at least three general cases that explain
this improved performance.
