AUTHORS: Harish Kumar B. T., Vibha L., Venugopal K. R.

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ABSTRACT: -Entrepreneurs are investing heavily on marketing and promoting business through the websites to enhance their online reputation and draw the attention of the web users. Website structure plays the vital role in attracting the web users. Creating personalized website structure for individual user by restructuring the web site structure is a tedious and endless job. If the users do not find the required information easily in the websites, then users abandon such websites. Hence, personalized recommendation of web pages to the web users increases the user’s interest and the time they spend in the website. Personalization is the process of creating customized participation of users to a website, rather than providing a broad participation. Personalization allows the website to present the users with the unique participation bespoke to their demands and passion. Personalized recommendation is a challenging task, which has drawn the focus of many researchers. Personalization has to trace the behavior of individual users. Usage behavior can be traced by observing the individual navigation patterns using web log file of the specific website. This method requires session identification, clustering sessions into similar clusters and building a model for personalized recommendations using access time length and frequency of access. Most of the existing works on this topic have used K-Means clustering with Euclidean distance. K-Means suffers from choosing the initial random center and sequence of page visits is not considered. The proposed research work uses Group Average Agglomerative Hierarchical Clustering (GAAHC), with Modified Levenshtein Distance (MLD) and page rank using access time length and the frequency of page access

KEYWORDS: Personalization, Recommendation, Agglomerative, Clustering, Levenshtein.

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