Intelligent and Adaptive Web Page Recommender System

Abstract

In this manuscript, an Intelligent and Adaptive Web Page Recommender System is proposed that provides personalized, global and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: Uniformity and Recommendation strength. The system continuously tracks the user’s responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset which is a significant improvement over the 70% F1 measure reported by Automatic Clustering-based Genetic Algorithm, the prior web recommender system.