Intelligent and Adaptive Web Page Recommender System

Geeta Rani, Vijaypal Singh Dhaka, Sonam, Upasana Pandey and Pradeep Kumar Tiwari
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Geeta Rani: Manipal University Jaipur, India
Vijaypal Singh Dhaka: Manipal University Jaipur, India
Sonam: Manipal University Jaipur, India
Upasana Pandey: IMS Engineering College, Ghaziabad, India
Pradeep Kumar Tiwari: Manipal University Jaipur, India

International Journal of Web Services Research (IJWSR), 2021, vol. 18, issue 4, 27-50

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.

Date: 2021
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