Intelligent and Adaptive Web Page Recommender System

Intelligent and Adaptive Web Page Recommender System

Geeta Rani, Vijaypal Singh Dhaka, Sonam, Upasana Pandey, Pradeep Kumar Tiwari
Copyright: © 2021 |Pages: 24
DOI: 10.4018/IJWSR.2021100102
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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.
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2. Prior Works

The study of prior works shows the significant contributions from researchers, in developing the WPR systems.

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