This paper is a survey of recent work in the field of web recommendation system for the benefit of research on the adaptability of information systems to the needs of the users. This issue is becoming increasingly important on the Web, as non-expert users are overwhelmed by the quantity of information available online, while commercial Web sites strive to add value to their services in order to create loyal relationships with their visitors-customers. This article views to provide a remedy for the negative effects of the traditional one-size-fits-all approach is to enhance the system\'s ability to adapt its own behavior to the user?�?s characteristics, such as goals, tasks, interests, that are stored in user profiles by implementing a variety of algorithms. The enormous content of information on the World Wide Web makes it obvious candidate for Web Recommendation System research. Web based application facing with large amount of data. In order to produce the portal usage patterns and user behaviors, Web recommendation system consists of three main phases, namely Data Preprocessing, Pattern Discovering and Pattern Analysis. Server log files become a set of raw data where it must go through with all the Web recommendation system phases to produce the final results. Here, Web recommendation system, approach has been combining with the basic Association Rules, Apriori Algorithm to optimize the content of the E-application portal. Finally, this paper will present an overview of results analysis and can use the findings for the suitable valuable actions.