【作者】 韩爽； 王衡；

【Author】 Shuang Han+, Heng Wang Lab of HCI and Multimedia, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

【机构】 北京大学信息科学技术学院人机交互与多媒体实验室；

【摘要】 随着计算机用户个人信息量日益扩大,如何帮助用户在系统中快速找到所需资源已成为当前智能交互行为模型的重要课题。过往的研究大多集中于个人信息管理上,力求以更加便于用户理解的个性化方式重新组织计算机资源结构。然而,由于上述系统往往需要用户大量的额外操作,并且重构用户的知识系统需要较为漫长的时间而不被用户采用。考虑到用户访问文件的主题性和目的性(用户往往会出于同一目的在同一时间段内同时访问多个同主题相关的文件),本文提出基于用户时间访问轨迹的文件推荐,并设计实现基于时间访问轨迹的智能文件推荐桌面工具(IFRDT),将根据用户访问文件的轨迹,针对用户当前正在访问的文件向用户推荐最有可能被访问到的同主题的其它文件,来减少用户查找所需资源花费的时间开销。用户在IFRDT中直接打开的文件占全部打开文件的一半以上。更多还原

【Abstract】 Since the amount of information on today’s computers grows, helping computer users to locate the required files in file systems has become an important topic in today’s intelligent interaction model research. Past research has mostly concentrated in PIM (Personal Information Management), to re-organize file hierarchies in a more understandable way for individual users. However, due to the numerous extra operations and the long period that needed for the re-organizing of users’ knowledge systems, the preceding applications can hardly be adopted by users. Considering that there would be a certain topic or purpose when user accessing files (a user may always view several files that related to the same topic during the same time), we propose file recommendation based on tracking user’s file operations. An intelligent file recommendation desktop toolkit (IFRDT) is implemented, which will track user’s file access history, recommend the most related files according to the currently being accessed file, to reduce time cost for finding desired information. Over 50% of all files opened by each testee during the tests are directly opened in IFRDT.更多还原