Web Links Prediction And Category-Wise Recommendation Based On Browser History
This work addresses the need for personalized web browsing assistance, but it is incremental as it builds on existing methods with minor improvements.
The paper tackles the problem of making web browsers more intelligent by predicting links during typing and recommending websites by category, achieving better accuracy through hyperparameter optimization of an existing URL classification model.
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.