论文标题
关于推荐系统的忠诚和语言解释的一致性
On Faithfulness and Coherence of Language Explanations for Recommendation Systems
论文作者
论文摘要
评论包含有关产品特征和用户兴趣的丰富信息，因此通常用于提高建议系统性能。具体而言，先前的工作表明，共同学习进行审查生成可以提高评级预测性能。同时，这些模型制作的评论是推荐说明，为用户提供了有关预测评分的见解。但是，尽管现有模型可能会产生流利的人类样评论，但尚不清楚评论在多大程度上完全揭示了共同预测的评级背后的基本原理。在这项工作中，我们执行一系列评估，以探究最先进的模型及其审查生成部分。我们表明，生成的解释是脆弱的，需要进一步评估，然后才能作为估计评级的字面原理。
Reviews contain rich information about product characteristics and user interests and thus are commonly used to boost recommender system performance. Specifically, previous work show that jointly learning to perform review generation improves rating prediction performance. Meanwhile, these model-produced reviews serve as recommendation explanations, providing the user with insights on predicted ratings. However, while existing models could generate fluent, human-like reviews, it is unclear to what degree the reviews fully uncover the rationale behind the jointly predicted rating. In this work, we perform a series of evaluations that probes state-of-the-art models and their review generation component. We show that the generated explanations are brittle and need further evaluation before being taken as literal rationales for the estimated ratings.
