WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM
DOI:
https://doi.org/10.29284/ijasis.8.1.2022.9-16Keywords:
Personalized recommendation, user profiles, ontology, stemming algorithm, feature extraction and semantic knowledge.Abstract
In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%.
Downloads
References
G. Matošević, J. Dobša and D. Mladenić, “Using machine learning for web page classification in search engine optimization,” Future Internet, vol. 13, no. 1, 2021, pp. 1-9.
K. Kikuchi, M. Otani, K. Yamaguchi and E. Simo‐Serra, “Modeling Visual Containment for Web Page Layout Optimization,” ComputerGraphics Forum, vol. 40, no. 7, 2021, pp. 33-44.
S. Singh and M. S. Aswal, “Constructing an effective ontology for web page recommendation,” International Journal of Web Engineering and Technology, vol. 16, no. 2, 2021, pp. 86-112.
G. Rani, V. S. Dhaka, U. Pandey and P. K. Tiwari, “Intelligent and AdaptiveWeb Page Recommender System,” International Journal of WebServicesResearch, vol. 18, no. 4, 2021, pp. 27-50.
J. Kiesel, L. Meyer, F. Kneist, B. Stein and M. Potthast, “An EmpiricalComparison of Web Page Segmentation Algorithms,” European ConferenceonInformation Retrieval, 2021, pp. 62-74.
C. Dang, H. Randrianarivo, R. Fournier-S'Niehotta and N. Audebert, “Web Image Context Extraction with Graph Neural Networks andSentence Embeddings on the DOM tree,” arXiv preprintarXiv: 2108.11629.
S.E. Oliver, “The advisory committee on immunization practices’ Interim recommendation for use of moderna COVID-19 vaccine-United States, December 2020,” MMWR Morbidity and mortality weekly report, vol. 69, 2021, pp. 1-4.
X. Chamberland-Thibeault and S. Hallé, “An Empirical Study of WebPage Structural Properties,” Journal of Web Engineering, vol. 20, no. 4, 2021, pp. 971-1002.
C. Lo, H. Yu, X. Yin, K. Shetty, C. He, K. Hu, J. M. Platz, A. Ilardi and S. Madhvanath, “Page-level Optimization of e-Commerce ItemRecommendations,” Fifteenth ACM Conference on Recommender Systems, 2021, pp. 495-504.
G. Deepak, B. N. Shwetha, C. N. Pushpa, J. Thriveni and K. R. Venugopal, “A hybridized semantic trust-based framework for personalized web page recommendation,” International Journal of Computers and Applications, vol. 42, no. 8, 2020, pp. 729-739.
X. Zhang and H. Wu, “PageRank Algorithm and HITS Algorithm in WebPageRanking,” International Conference on Application ofIntelligent Systems in Multi-modal Information Analytics, 2021, pp. 389-395.
T. Khede and A. Sharma, “Bio-Geography Based Page Prediction UsingWebMining Feature,” International Journal of Science, Engineering and Technology, vol. 7, no. 6, 2019, pp. 1-5.
R. Manikandan and V. Saravanan, “A novel approach on Particle Agent Swarm Optimization (PASO) in semantic mining for web page recommender system of multimedia data: a health care perspective,” Multimedia Tools and Applications, vol. 79, no. 5, 2020, pp. 3807-3829.
B. Pavithra and M. Niranjanamurthy, “Web page recommendation using genetic and feed forward association rule on web-log features,” Journal of Computational and Theoretical Nanoscience, vol. 17, no. 9-10, 2020, pp. 4462-4467.
Q. Hao, T. Luo and G. Huzhang, “Re-ranking with constraints on diversified exposures for homepage recommender system,” arXiv preprint arXiv:2112.07621. 2021, pp. 1-10.
N. Kumar, M. Gupta, D. Sharma and I. Ofori, “Technical Job Recommendation System Using APIs and Web Crawling,” Computational Intelligence and Neuroscience, vol. 2022, 2022, pp. 1-13.
G.S. Chhatwal and G. Deepak, “IEESWPR: An Integrative Entity Enrichment Scheme for Socially Aware Web Page Recommendation,” Data Science and Security, 2022, pp. 239-249.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
