2019/02/21 by Ashadullah Shawon, Syed Tauhid Zuhori, Shawon, Ashadullah +5
Computer Science · Mathematics · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Text and Document Classification Technologies #Web Data Mining and Analysis #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1902.08496
preprint
arxiv created 2019/02/21 · openalex publication_date 2019/02/21 · arxiv updated 2019/02/25 · openalex created_date 2019/04/11 · openalex updated_date 2026/07/28
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.