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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 |Volume: 18 |Issue: 4 |Pages: 24
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781799859314|DOI: 10.4018/IJWSR.2021100102
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MLA

Rani, Geeta, et al. "Intelligent and Adaptive Web Page Recommender System." IJWSR vol.18, no.4 2021: pp.27-50. http://doi.org/10.4018/IJWSR.2021100102

APA

Rani, G., Dhaka, V. S., Sonam, Pandey, U., & Tiwari, P. K. (2021). Intelligent and Adaptive Web Page Recommender System. International Journal of Web Services Research (IJWSR), 18(4), 27-50. http://doi.org/10.4018/IJWSR.2021100102

Chicago

Rani, Geeta, et al. "Intelligent and Adaptive Web Page Recommender System," International Journal of Web Services Research (IJWSR) 18, no.4: 27-50. http://doi.org/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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