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User and item-aware estimation of review helpfulness

机译：用户和项目感知估算审查助人

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摘要

In online review sites, the analysis of user feedback for assessing its helpfulness for decisionmaking 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.
机译：在在线审查网站中，通常通过在本地研究个人评论的属性来进行评估其对决策乐观的用户反馈的分析。但是，应考虑全局属性，精确地评估用户反馈的质量。在本文中，我们调查偏差在审查属性中的作用，作为“核心”反馈“出于核心”反馈有助于物品评估的乐观决定因素。我们提出了一种新颖的借助估算模型，其延伸了以前的估计模型，分析了与同一个人撰写的评审，长度和极性的偏差分析，或关于同一项目的评价。在Yelp社交网络中提取的两个大型评论的评论中进行的回归分析表明，在审查长度和评级中基于用户的偏差显然影响了感知乐于助人。此外，在相同数据集上的实验表明，通过增强用于评级估计的高质量数据的选择，我们的助人估算模型的集成提高了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的有效工具进行决策。

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