MODELING AND TESTING OF A NOVEL WEB BASED RECOMMENDATION SYSTEM

Sowmya K.Menon, Varghese Paul, M.Sudheep Elayidom, Yashwant Sinha

Abstract: In this era of social networking and its high demand, there is indeed a real need of an efficient system that recommends good websites of interest to each user. Here we are analyzing the user,s browsing behavior habits. After making clusters by the system it will recommend the users the other web sites belonging to same cluster behavior. Here the browsing habits of a group of users are analyzed and the users are grouped to different clusters in the server such that the users in the same cluster have similar browsing habits to visit similar web sites. The core idea is that the system will recommend the frequent web sites visited by other users in the same cluster to a typical user of a cluster. The entire system is implemented over a cloud framework of many users and a cloud server based on the Google App Engine. In this paper, we focus more on the testing the effectiveness of the proposed recommendation system. To get the accuracy we are comparing the browsing behavior of the users in the database and the sites which is going to be recommended.

Keywords: Clustering, Google App Engine, Web Recommendation System.

DOI: https://doi.org/10.15623/ijret.2017.0601012