“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems (2021)

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.

## introduction conversational recommendation systems (crss) are dialog-based systems that aim to refine a set of options over multiple turns of a conversation, envisioning more natural interactions and better user modeling than in non-conversational approaches. however, the resulting dialogs still do not necessarily reflect how real conversations unfold. most crss fall into two categories: they either frame the problem as a slot-filling task within a predefined feature space, such as @xcite ; @xcite ; @xcite , which is closer to how people make decisions but not as flexible as real conversations; or they elicit preferences by asking users to rate specific items, such as @xcite , which is in-dependent of a feature space but not as natural to users. figure 1 : an example of our system transforming a critique into a positive preference and then using a customer testimonial to sell the user on a new option. when we examine situations involving real human agents @xcite , decisions typically require multiple rounds of recommendations by the agent and critiques by the user, with the agent continuously improving the recommendations based upon user preferences that can be inferred from such critiques. these inferences can be compared to the types of common sense inferences that have been studied recently with lms @xcite @xcite . however, use of lms for critique interpretation remains underexplored, despite the important role of critiques in communicating preferences-a very natural realworld task. working in the restaurant domain, we prompt gpt3 @xcite to transform a free-form critique (e.g., "it doesn't look good for a date") into a positive preference (e.g., "i prefer more romantic") that better captures the user's needs. compared with most previous work on common sense inference, which relies on manuallyconstructed question sets, our task presents an opportunity to study common sense inference within a naturally arising, real-world application. we test the effect of our novel critique interpretation method on the quality of recommendations using two different methods: one that matches the embedding of an input statement (e.g., "i prefer more romantic") to persuasive arguments found in customer reviews (e.g., "perfect for a romantic dinner"); and another one that fine-tunes bert @xcite in using an input statement to rank a given set of arguments. our work differs from previous critiquing-based systems that strongly limit the types of critiques that can be used @xcite and aligns with a recent trend in the crs literature towards more open-ended interactions @xcite . to the best of our knowledge, @xcite are the closest prior work investigating whether bert can be used for recommendations by trying to infer related items and genres. here, we focus specifically on critiqueto-preference inferences, aiming at more natural dialogs and better recommendations. our contributions are the following: 1. we propose a critique interpretation method that does not limit the feature space a priori; 2. we demonstrate that transforming critiques into preferences improves recommendations over two fold when matching embeddings and by 19-59% when finetuning an lm to rank recommendations, and present three possible explanations for this; and 3. we release a new dataset of user critiques in the restaurant domain, contributing a new applied task where common sense has great practical value. ## methods in this section, we describe three methods: a critique interpretation method (2.1), an embeddingsbased recommender (2.2.1), and an lm-based recommender (2.2.2). ## evaluation we run two ablation studies to evaluate the hypothesis that critique interpretation would be beneficial to the overall recommendation approach. first, we analyze our embeddings-based recommender, f cos , to check whether the performance of f pref @xmath0. secondly, we analyze our lm fine tuning-based recommender, f lm , to check if f pref @xmath1. finally, we discuss qualitative differences between the tested arms. ## conclusion & future work in this paper, we presented an open-ended approach to content-based recommendations for crs. we developed a novel critique interpretation method that uses gpt3 to infer positive preferences from freeform critiques. we also developed two methods for retrieving recommendations: one that matches embeddings and another that fine-tunes bert for the task. we ran two ablation studies to test if transforming critiques into positive preferences would yield better recommendations, confirming that it improves performance across both methods. finally, we described three critique patterns that cause systematic errors in recommendation search if critique interpretation is turned off. for future work, we will strive to use critiques to identify and remove unsuitable restaurants; we speculate that the sparsity of customer reviews generally makes it harder to "rule out" than to "rule in." we will also study other issues such as when to ask clarification questions to resolve ambiguity in the scope of a critique.