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 se
nse 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.
