Evaluation of macro and parallel data processing tools affecting the improvement of performance to extract user interests

Abstract

Since knowledge mining is used as one of the ways to extract knowledge from large volumes of data, the present study was carried out with the aim of evaluation of macro and parallel data processing tools affecting the improvement of performance to extract user interests. Recommending systems use the method of automatic recommendation to users based on their interest and use statistical techniques and knowledge discovery to recommend products / services to users to face information overload. The advantage of recommending system is that makes user to not get tired of dealing with repetitive and nonuseful information and quickly find right and interesting information among the items. In this research, an approach of content recommending system was provided based on existing log s of users on proxy server based on their interest which was effective in both the speed of search in sites intended by users to achieve their demand and significantly reduced the network traffic throughout the organization and network bandwidth was able to have less traffic