Surfing the Network for Ranking by Multidamping

Product Discription

 The web is an important source of information retrieval now-a days, and the users accessing the web are from different backgrounds. The usage information about users are recorded in web logs. Analyzing web log files to extract useful patterns is called web usage mining. Web usage mining approaches include clustering, association rule mining, sequential pattern mining etc., To facilitate web page access by users, web recommendation model is needed. So the Interest in the analysis of user behavior on the Web has been increasing rapidly. This increase stems from the realization that added value for Web site visitors is not gained merely through larger quantities of data on a site, but through easier access to the required information at the right time and in the most suitable form. Estimates of Web usage expect the number of users to climb up to 945million by 2004 [12]. The majority of these users are non-expert and it difficult to keep up with the rapid development of computer technologies, while at the same time they recognize that the Web is an invaluable source of information for their everyday life. The increasing usage of the Web also accelerates the pace at which information becomes available online.

In re-ranking of web page several models are prepare which utilize different techniques.  For this new technique different outcomes are obtain which have probability to predict the chance of the next web page. So targeting the most probable page makes the very low precision. The basic component here is to collect features of the web so that prediction is more effective then the unorganized data. For the optimization of the prediction it is require that all the feature of the website should utilize while this increase the execution time with space. Main feature of web mining is web log utilizing for this different web log technique was used. So this act as the powerful feature of the web mining which can re-rank the web page of the user in advance with high precision.

Whole work is divide into two steps first is the module builing this can be understand as the preparation of the model for the page re-ranking such as creating the system for the  web logs. After this step next step is to test the model where different surfers are pass in the model and it will predict the next page by the model.

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