A Least Square Approach to Analyze usage data for Effective web Personilization

Web server logs have abundant information about the nature of users accessing it. 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. 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 [11, 12, 13].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.