The Research And Implement Of Web Mining Technology On VOD Application Server

With the popular of Digital Television and the development of VOD System, people can ramble through the immense video ocean of cable TV network. As the result of video-Resource explosion, many people with little experience are often lost in the network, and they feel very distressed because of not finding what they really need. So the puzzle we face now is how to provide better Web personalized recommendation according to people's special need. The aim of Web personalized recommendation is to provide relative pages which people may be interested in when they access VOD site. It is the main method of improving service quality and access frequency.First of all,the thesis sums the Web mining theory, and deeply probes into the concept, categories and studies of personalized recommendation, then it analyses the similarities and differences between the cable network VOD System Web mining technology and Internet-based Web mining technology. Besides, it analyses and probes into the typical ass rule mining algorithm on which a new algorithm is based. The thesis summarizes the problems involved in personalized recommendation based on Web mining are as follows:Web mining, personalized recommendation techniques and realizing personalized recommendation based on mining association rule.The paper analyzes the concept of the Apriori algorithm, and proposes the advanced Apriori algorithm which applies to Web personalized recommendation. Besides it takes a particular introduction through examples,and it takes meticulous comparisons and analyses between Apriori algorithm and the advanced algorithm from the point of view of time and space.The paper deeply addresses that how to use the mined association rules to achieve Web personalized recommendation and the principles and steps of recommendation prototype. It proposes the architecture of the Web personalized recommendation System, the description of the mining module and the design of database, then finds the relative factors which affect the accuracy of recommendation through experiments.At last, it demonstrates the Web personalized recommendation and shows the results.