Web server logs have abundant information about the nature of users accessing it. The analysis of the user’s current interest based on the navigational behavior may help the organizations to guide the users in their browsing activity and obtain relevant information in a shorter span of time (Sumathi and Padmaja, 2010). Web usage mining is used to discover interesting user navigation patterns and can be applied to many real-world problems, such as improving web sites/pages, making additional topic or product recommendations, user/customer behavior studies, etc (Ratanakumar, 2010). Web usage mining, in conjunction with standard approaches to personalization helps to address some of the shortcomings of these techniques, including reliance on subjective lack of scalability, poor performance, user ratings and sparse data (Mobasher et al., 2002; Eirinaki and Vazirgiannis, 2003; Khalil et al., 2008; Forsati et al., 2009; Mobasher et al., 2001). But, it is not sufficient to discover patterns from usage data for performing the personalization tasks. It is necessary to derive a good quality of aggregate usage profiles which indeed will help to devise efficient recommendation for web personalization (Cooley et al., 1997; Srivatsava et al., 2000; Agarwal and Srikant, 1994). Also, the unsupervised and competitive learning algorithms has help to efficiently cluster user based access patterns by mining web logs (Hartigan and Wong, 1979; Ng et al., 2007; Memon and Dagli, 2003). This paper presents and experimentally evaluates a technique for finely tuning user clusters based on similar web access patterns on their usage profiles by approximating through least square approach. Each cluster is having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users. Experimental results indicate that using the generated aggregate usage profiles with approximating clusters through least square approach effectively personalize at early stages of user visits to a site without deeper knowledge about them.
Key words: Aggregate usage profile, least square approach, web personalization, recommender systems, expectation maximization.
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