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. 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. 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. 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.