The Application Of Data Mining Technology In Web Site Personalized Recommendation

Web is so complicated, order less and dynamic that people cannot search the information they need quickly and conveniently. Web mining is attempting to find implicative, unknown, potential useful and non-trivial schema from the innumerable Web files and the mass data collected through users'browsing. Web mining is an extension of traditional Data Mining technology used in Web environment.During the design and implementation of a Web site, it would be more effective and specific to meet users' needs if Web personalized recommendation could be implemented based on users' interests. Web usage mining of users' browsing behaviors could implement personalized recommendation effectively.After thoroughly compared and researched on the differences of Web content mining, Web structure mining and Web usage mining, it demonstrated that Web usage mining was significant for the personalized recommendation of Web site. By analyzing the general process of Web mining, the personalized recommendation plan was developed for Web site optimization.Compared advantages and shortcomings of collecting Web data at server, proxy and client sides, analyzed the limitations of obtaining users' browsing behaviors from different data sources, and finally selected the server side as data mining source. Further analyzed the characteristics of server side data source, a preprocessing method to these log data was introduced, the key processes included data cleaning, user identification and session identification. These key processes ensured the most accurate data can be introduced into later mining stage.Analyzed the pages topological structure and accessing time, swept out the users'uninterested pages and enhanced the accuracy of the data source. Analyzed the characteristics of the association rule for personalized recommendation, and introduced a data mining method which is suitable for Web page personalized recommendation. Moreover, selected the data storage structure and the association rule mining algorithm satisfied this method. Meantime, took into account the page support degree, the page accessing time and the distance between pages, a weighted method of page recommendation was provided which further improved the accuracy of recommending page. Finally, a personalized recommendation service for a Web site was implemented based on above scheme. The Web site's running result shows that the scheme can meet the original expectation: during users'browsing, it can provide personalized recommendation tallied with users'browsing behaviors and interests.