Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales. While many reviews are informative, others provide little value and may contain spam, excessive appraisal, or unexpected biases. With the large volume of reviews and their uneven quality, the problem of detecting helpful reviews has drawn much attention lately. Existing methods for identifying helpful reviews primarily focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. Moreover, the helpfulness votes suffer from scarcity for less popular products and recently submitted (a.k.a., cold-start) reviews. To address these challenges, we introduce a dataset and develop a model that integrates the reviewer's expertise, derived from the past review history of the reviewers, and the temporal dynamics of the reviews to automatically assess review helpfulness. We conduct experiments on our dataset to demonstrate the effectiveness of incorporating these factors and report improved results compared to several well-established baselines.

翻译：有用评论对于电子商务服务的成功至关重要，因为它们能帮助客户快速做出购买决策，并有利于商家的销售。尽管许多评论信息丰富，但其他评论价值有限，可能包含垃圾信息、过度评价或意外偏见。由于评论数量庞大且质量参差不齐，检测有用评论的问题近来备受关注。现有识别有用评论的方法主要聚焦于评论文本，而忽略了两个关键因素：（1）评论发布者是谁；（2）评论发布时间。此外，对于不太热门的产品和新近提交（即冷启动）的评论，有用性投票存在稀缺性问题。为解决这些挑战，我们引入了一个数据集，并开发了一个模型，该模型整合了从审稿人既往评论历史中推导出的审稿人专业度以及评论的时间动态特性，以自动评估评论的有用性。我们在该数据集上进行了实验，证明了纳入这些因素的有效性，并报告了相较于多个成熟基准方法更优的结果。