  A web browser should not be only for browsing web pages but also help users
to find out their target websites and recommend similar type websites based on
their behavior. Throughout this paper, we propose two methods to make a web
browser more intelligent about link prediction which works during typing on
address-bar and recommendation of websites according to several categories. Our
proposed link prediction system is actually frecency prediction which is
predicted based on the first visit, last visit and URL counts. But recommend
system is the most challenging as it is needed to classify web URLs according
to names without visiting web pages. So we use existing model for URL
classification. The only existing approach gives unsatisfactory results and low
accuracy. So we add hyperparameter optimization with an existing approach that
finds the best parameters for existing URL classification model and gives
better accuracy. In this paper, we propose a category wise recommendation
system using frecency value and the total visit of individual URL category.
