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arXiv · 2109.07576

"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems

Victor S. Bursztyn·Jennifer Healey·Nedim Lipka·Eunyee Koh·Doug Downey·Larry Birnbaum
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Abstract

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.

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Victor S. Bursztyn, Jennifer Healey, Nedim Lipka, Eunyee Koh, Doug Downey, Larry Birnbaum. 2021-09-15. "It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems. https://arxiv.org/abs/2109.07576

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