通过批评与企业互动
Interacting with Explanations through Critiquing
摘要：
使用个性化的解释来支持推荐已被证明可以增加信任和感知质量。然而，为了实际获得更好的推荐，需要有一种方法让用户通过与解释交互来修改推荐标准。我们提出了一种新的技术，使用方面标记，学会从评论文本中生成个性化的建议解释，我们表明，人类用户显着更喜欢这些解释比那些产生的国家的最先进的技术。我们的工作最重要的创新是，它允许用户通过批评文本解释来对推荐做出反应：删除（对称添加）他们不喜欢或不再相关的某些方面（对称添加）。系统根据评论更新其用户模型和所得到的推荐。这是基于一种新的无监督的批评方法，用于单步骤和多步骤的批评与文本解释。两个真实世界的数据集上的实验表明，我们的系统是第一个实现良好的性能，在适应多步critiquing表达的偏好。
英文摘要：
Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques. Our work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation: removing (symmetrically adding) certain aspects they dislike or that are no longer relevant (symmetrically that are of interest). The system updates its user model and the resulting recommendations according to the critique. This is based on a novel unsupervised critiquing method for single- and multi-step critiquing with textual explanations. Experiments on two real-world datasets show that our system is the first to achieve good performance in adapting to the preferences expressed in multi-step critiquing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
