用户偏好模型的构建方法是制约空间信息智能分发理论取得进展的关键瓶颈。现有的空间信息智能分发算法和系统存在空间范围定位不准确、效用度计算存在偏差等缺陷,且大多关注用户的检索行为对偏好模型的贡献却均未考虑时间因素的影响,也很少注意到用户反馈的作用。鉴于此,对现有文献的理论和算法进行扩展,通过引入区域数、兴趣度、兴趣度密度等概念和算法,以及权值衰减函数和用户信息反馈等动态化因素,使模型能够更为准确、及时地随着用户偏好特征的变化进行修正。试验表明,相较于传统的静态模型而言,该模型能够更为有效地反映用户偏好特征的变化。 更多还原
【Abstract】 User profile modeling method is the key bottleneck that restricts theory of intelligent distribution of spatial information to progress.Existing algorithms and systems of intelligent distribution of spatial information have drawbacks of inaccurate spatial location and biased utility,etc.and are mostly concerned on the contribution of the user’s retrieval behavior to the profile model,but not consider time factors at all,and pay little attention to the role of user feedback.In view of this,the th... 更多还原