论文标题
建议的评论有多么有用？批判性审查和潜在的改进
How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements
论文作者
论文摘要
我们调查了越来越多的工作，试图通过使用审查文本来改善推荐系统。通常，这些论文认为，由于评论“解释“用户”的意见，它们对于推断预测评级或购买的基本维度应该很有用。合并评论的方案范围从简单的正规化器到神经网络方法。我们的最初发现显示了报告结果的几个差异，部分原因是（例如）尽管实验设置或数据预处理进行了变化，但（例如）（例如）（例如）复制结果。首先，我们尝试进行全面的分析来解决这些歧义。进一步的调查要求讨论有关用户评论的“重要性”提出建议的更大问题。通过广泛的实验，我们观察到了几种情况，即最先进的方法无法超越现有的基准，尤其是当我们偏离一些偏差定义的设置时，在这些设置中，评论很有用。我们通过为我们的观察提供了假设来结论，该假设试图在哪些条件下进行审查可能会有所帮助。通过这项工作，我们旨在评估该领域的发展方向并鼓励强大的经验评估。
We investigate a growing body of work that seeks to improve recommender systems through the use of review text. Generally, these papers argue that since reviews 'explain' users' opinions, they ought to be useful to infer the underlying dimensions that predict ratings or purchases. Schemes to incorporate reviews range from simple regularizers to neural network approaches. Our initial findings reveal several discrepancies in reported results, partly due to (e.g.) copying results across papers despite changes in experimental settings or data pre-processing. First, we attempt a comprehensive analysis to resolve these ambiguities. Further investigation calls for discussion on a much larger problem about the "importance" of user reviews for recommendation. Through a wide range of experiments, we observe several cases where state-of-the-art methods fail to outperform existing baselines, especially as we deviate from a few narrowly-defined settings where reviews are useful. We conclude by providing hypotheses for our observations, that seek to characterize under what conditions reviews are likely to be helpful. Through this work, we aim to evaluate the direction in which the field is progressing and encourage robust empirical evaluation.
