Artificial Immune Based Web Page Recommendation Using Bootstrap Bagging Trained Model

Abstract

Presence of similar type of digital content on websites increases the visitor interest. But getting irrelevant content as per user current requirement reduces the retention on the website. So web page recommendation plays an important role on the website. This paper has proposed a model Artificial Immune and Bootstrap Bagging based Webpage Recommendation (AIBBWR) that utilizes website data for the page recommendation. A weblog feature was used to train the Bootstrap Bagging model. In order to maintain randomness in the work artificial immune genetic algorithm was used that utilizes trained Bootstrap Bagging model with web content feature for the page recommendation. Experiment was done real and live website. Results were compared on different parameters and it was found that proposed model AIBBWR has improved work performance.