An efficient periodic web content recommendation based on web usage mining

Now a day's use of internet has been increased tremendously, so providing information relevant to a user at particular time is very important task. Periodic web personalization is a process of recommending the most relevant information to the users at accurate time. In this paper we are proposing an improved personalize web recommender model, which not only considers user specific activities but also considers some other factors related to websites like total number of visitors, number of unique visitors, numbers of users download data, amount of data downloaded, amount of data uploaded and number of advertisements for a particular URL to provide a better result. This model consider user's web access activities to extract its usage behavior to build knowledge base and then knowledge base along with prior specified factors are used to predict the user specific content. Thus this advance computation of resources will help user to access required information more efficiently and effectively.