The Research Of Personalized Recommendation Based On Web Mining

With the development of network technology, there is a requirement that use the data mining technologies to mine the useful resource from massive network information. Web has come to be a hot research area in the information search field. Now the popular and mature IR(information recommandation) technologies settle these problems in a network information objected method. However, the network information oriented IR technologies cannot understand and execute users'personal need. In this dissertation, we bring forward a new model for information recommendation in Web, user-oriented information recommendation model.Firstly,this article introduces basic definition of data mining and Web data mining and their related content.By the related reseach about Web personalization recommandtion, we designs a personalized information recommendation system based on data mining(PIRS),and gives a recommendation strategy and algorithms respectively. PIRS includes:data preprocessing module,mining treatment module,on-line recommendation module and user interface module. We put forward on-line recommendation strategy in PIRS ,and different recommendation algorithms according different users. The system gives two kinds of recommendation algorithms based on association rule mining and user's transaction pattern clustering. The recommendation method based on association rule mining is to take advantage of user's own transction pattern file to produce assembly tree,then association rules by user's session and assembly tree,finally recommendation sets ensues. It possessers two characteristics:fast speed and exactness. However,it does not fit new user,less-time visitors and users who need fresh information. As to the second kind is to assemble similar user's transaction patterns,produces user's transaction clustering patterns,then match user's session and user's transaction clustering patterns, finally produce recommendation sets. It fits new users,less-time visitor and users who need fresh information.