Analysis of Effectiveness of the Browsing Behavior of Web User using Sequence Weight Knowledge

Abstract

A number of web users worldwide regularly follow the web to share their information, converse various topics, share, stay connected and obtain information. As a result, enormous amounts of data are generated by the web users and then one could employ this data to obtain useful predictions of certain web user related behavior. There are many platforms where web users could communicate and exchange information. These platforms comprises of social networks, and digital communication networks. In this research paper, the effectiveness of the browsing behavior of web user is analysed using sequence weight learning. The outcome shows that the proposed personalized recommendation framework using the Competence Scoring process for sequence weight learning able to achieve significantly higher accuracy. The investigation result of web uses also shows the personalized framework predicts future items reliably and could be employed to automatically recommended next-items to indented web users.