目的 互联网信息量的急速增长使得人们需要花费大量时间从搜索引擎召回的结果中浏览自身感兴趣的内容，结合用户的搜索日志信息和社交平台信息，提出一种分层的实时偏好挖掘模型，为用户提供个性化搜索服务。方法 在系统分析偏好挖掘的国内外研究现状的基础上，针对足球视频，提出一种分层权重无向图（HWUG）用户偏好模型，充分考虑用户偏好之间的关联信息，通过获取用户在足球领域的显式和隐式反馈信息，提取反馈信息中的偏好标签和偏好动作，并引入时间衰减因子，实现用户足球偏好的实时计算。结果 算法已经应用在搜球网（www.findball.net）的个性化检索结果排序和视频推荐上，并已经取得了很好的效果。结论 实验结果表明，结合特定领域的知识，基于分层无向权重图模型的偏好挖掘算法能更准确和实时反映用户的足球偏好。

Objective With the explosive growth of internet information, although search engines can provide convenient information retrieval services, people still have to spend a lot of time and energy to find the information they are interested in from millions of search results. Therefore, it is significant to mine user's preference from the search engine's interactive information and to provide personalized search service. Method Our method is based on a systematic analysis of current researches on user preference mining. We propose a novel preference model named HWUG, which fully considers the relationship between the user preference information. It can extract preference label and action from the user's explicit and implicit feedback information. A time attenuation factor is brought into the historic preference information in order to provide the real-time computation of preference information. Result The proposed methods have been used in the soccer video search engine(www.findballl.net)and achieved good results in personalized video retrieval and recommendation. Conclusion The experimental results show that the combination of domain-specific knowledge, based on a hierarchical graph model undirected weighted preferece mining algorithm can more accurately reflect the user's soccer preferences in real-time.