In online review sites, the analysis of user feedback for assessing its helpfulness for decision-making is usually carried out by locally studying the properties of individual reviews. However, global properties should be considered as well to precisely evaluate the quality of user feedback. In this paper we investigate the role of deviations in the properties of reviews as helpfulness determinants with the intuition that "out of the core" feedback helps item evaluation. We propose a novel helpfulness estimation model that extends previous ones with the analysis of deviations in rating, length and polarity with respect to the reviews written by the same person, or concerning the same item. A regression analysis carried out on two large datasets of reviews extracted from Yelp social network shows that user-based deviations in review length and rating clearly influence perceived helpfulness. Moreover, an experiment on the same datasets shows that the integration of our helpfulness estimation model improves the performance of a collaborative recommender system by enhancing the selection of high-quality data for rating estimation. Our model is thus an effective tool to select relevant user feedback for decision-making.

中文翻译：

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用户和项目感知的评价有用性估计

在在线评论站点中，通常通过本地研究单个评论的属性来分析用户反馈以评估其对决策的帮助。但是，还应考虑全局属性，以精确评估用户反馈的质量。在本文中，我们凭直觉认为“超出核心”反馈有助于项目评估，从而考察了偏差在评论属性中的作用，这是有用性的决定因素。我们提出了一个新颖的帮助评估模型，该模型扩展了以前的评估模型，该模型分析了关于同一人或同一项目的评论的等级，长度和极性的偏差。对从Yelp社交网络提取的两个大型评论数据集进行的回归分析表明，评论长度和评分中基于用户的偏差明显影响了感知的帮助。此外，在相同数据集上进行的实验表明，我们的帮助度评估模型的集成通过增强对评级评估的高质量数据的选择来提高协作推荐系统的性能。因此，我们的模型是选择相关用户反馈进行决策的有效工具。