论文标题
支持以互动驱动的指标和偏见降低建议的偶然发现和平衡分析在线产品评论
Supporting Serendipitous Discovery and Balanced Analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions
论文作者
论文摘要
在这项研究中，我们研究了如何支持在线产品评论的偶然发现和分析如何鼓励读者在做出购买决策之前对评论进行更全面的探索。我们提出了两种干预措施 - 探索指标，可以通过视觉指标和偏见缓解模型来帮助读者理解和跟踪其探索模式，该模型打算通过暗示情感和语义上多样化的评论来最大程度地提高知识发现。我们设计，开发和评估了一个称为Serendyze的文本分析系统，并在其中整合了这些干预措施。我们要求100名群众使用Serendyze根据产品评论做出购买决策。我们的评估表明，探索指标使读者能够以平衡的方式有效涵盖更多评论，而偏见模型的建议影响了读者做出自信数据驱动的决策。我们讨论用户代理和信任在文本级分析系统中的作用及其在审查探索之外的领域中的适用性。
In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions -- Exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.
