An efficient web recommender system based on approach of mining frequent sequential pattern from customized web log preprocessing

Abstract

Internet has penetrated into every areas of society and has also become a huge, pervasive distribution and global information service center. In a real world The only option is to capture the attention of the user and provide them with the recommendation list to match the needs of user and keep their attention in their web site. Web usage mining is a kind of data mining method that provide Smart personalized online services such as web recommendations, it is usually necessary to model users' web access behavior. Web usage mining includes three process, namely, preprocessing, pattern discovery and pattern analysis. The data reduction is achieved through data preprocessing. The aim of discovering frequent sequential access patterns in Web log data is to obtain information about the navigational behavior of the users. In the proposed system, an efficient sequential pattern mining algorithm is used to identify frequent sequential web access patterns. The access patterns are retrieved from a Graph, which is then used for matching and generating web links for recommendations.