Recommended Wum-based Personalization Of Smart Technology Research

With the fast-growing Internet and the maturation of WWW (world wide web), applications based on this technology are entering into every aspects of our society, the amount of the information which can be made use of become more and more larger , either to the type of it. Inevitably the transaction information of humankind is being electrified. It is difficult for the user to search out the needed information because of the inorganization and largeness of the information and the universality of the recource in Internet. Further more, the information access and search engine can not resolve these problems efficiency for their inhere defect.The amalgamation of the data mining and WEB offer a new way to resolve the problem. This paper try to made in-depth analysis and research on the WEB logs data by WEB data mining resulting in a user' s transaction pattern, and achieve the intelligent services of personalization recommendation. The contents of this dissertation are as follows:(1) We review the origin and background of data mining technology; introduce current status of international and domestic research on data mining.(2) We made in-depth analysis and research on the systematic structure of WEB date mining, gave outline of WEB date mining, definition and category of WEB date mining, and described general process of data mining for WEB logs.(3) To introduce the general structure and definition of the data preprocessing phase of WEB logs mining. The transaction identification based on reference length >maximal forward reference and time windows are proposedrespectively .(4) To discuss the clustering methods for two user transaction patterns that are user' s navigation-content transaction based on maximal forward reference and the user' s content-only transaction respectively. In the former, the similarity measures between user' s transaction patterns attempt to incorporate with the structures of WEBsite and the URLs involved . In the latter , the similarity measures use direct paths, the common ancestors and the common descendants to clustering user' s transaction patterns for the online personalized intelligent recommendation services.(5) To propose a intelligent service method on personalized recommendation based on user' s transaction patterns and user' s current navigational activity, the overall process of which can be divided into two parts: offline part and online part. In offline, WEB mining tasks can execute in the logs of WEB service resulting in a user' s transaction pattern file. In online, the candidate URLs for recommendation can be determined by matching association rules in the aggregating tree or URL clusters with the current active session for the intelligent services of personalization recommendation. The advantage and shortcoming of each in two methods are discussed. The experiments demonstrate that our approach is applicable and effective.