面向满意度预测的滑鼠行为量化分析方法

Satisfaction Prediction Oriented Quantitative Mouse Movement Analysis

现有信息检索研究领域中,衡量知识学习质量和信息获取精度的核心标准是信息与用户需求的相关性(量化指标为相关度).然而,这一测度往往无法直观反映用户对信息伪反馈的"满意度".相比于多媒体(文字语言、图像、音频和视频)之间可测可量的相关度,由用户主观认知驱动的满意度往往无法通过直观的量化方法予以获取和测量.针对这一问题,文中提出一种基于鼠标滑动(Mouse Movement,简称"滑鼠")运动学规律的"满意度"量化分析和预测方法.该方法集中于人类肢体活动驱动下的滑鼠滑行轨迹分析,借助复杂滑行过程中滑鼠呈现出的动力学能量,间接预测人类思维活跃的程度,以此估计用户接触特定信息伪反馈时隐式反射出的满意度.实验验证,该方法能够有效辅助信息检索过程中的用户体验分析.

In current study on Information Retrieval（IR）,the determination of quality of knowledge learning and precision of information acquisition heavily depend on the relevance（quantity is named as relevance degree）between user＇s need and information.However,the quantity normally is incapable of reflecting the satisfactoriness degree of users to pseudo information feedback.Compared to the scalable relevance degree among multi-medias,such as linguistic texts,images,audios and videos,the satisfactoriness,which is triggered and driven by human＇s subjective recognition,is not easily reachable.In particular,it is difficult to directly measure the satisfactoriness degree.To solve the problem,the paper proposes a Mouse-Movement-Law based on satisfactoriness analysis and measurement method.The method concentrates on trajectory analysis of mouse movement that is driven by human＇s physical activity.More importantly,the method detects the impetus of mouse movement（i.e.,momentum）during the course of sliding,by which it indirectly reflects the activity degree of the mind.The quantity of momentum,accordingly,is favorable to measurement of satisfactoriness toward pseudo information feedback.Experiments show that the method is effective in supporting analysis of user experience in the process of IR.