An intelligent internet information delivery system to evaluate site preferences

Due to the continual growth of the popularity of the Internet, commercial as well as industrial companies have been advertising their products and services via the Web, resulting in a drastic increase in the number of Web sites. With a huge amount of information available on various Web sites, it is important that the relevant and useful information favored by individual visitors is delivered to the destinations in a timely manner. The two traditional approaches for sorting web information including search engines and hierarchical indices require specific input by the visitors who may not have any specific favorite sites in mind. In most cases, site surfers are just “window-shopping” on the Internet, looking for “exciting” things. This paper proposes the development of an Intelligent Internet Information Delivery System (IIIDS) which is characterized by its machine learning capability based on the data of site spots “movements” by the users within the Web pages and then evaluates the site preferences of the relevant users by means of fuzzy logic principle. The development of IIIDS and the test of a prototype to evaluate its feasibility are covered in this paper