Abstract : The present study investigates the application of web mining methodologies to improve personalized recommendation systems within the context of electronic commerce. Web mining algorithms, such as association rule mining and clustering, facilitate the creation of precise recommendation models by examining user behavior and item characteristics using collaborative and content-based filtering methods. The study examines many obstacles, including the scarcity of data and issues around privacy, and puts up potential remedies such as matrix factorization and privacy-preserving techniques. This study seeks to enhance user engagement and commercial performance in e-commerce platforms through the incorporation of web mining techniques into recommendation systems.
Keywords : Web mining, Personalized recommendation systems, E-commerce, Collaborative filtering, Content-based filtering, Association rule mining, Hybrid recommendation systems
Cite : Bansal, D., Yamaganti, R., & Khan, S. (2024). Web Mining Techniques For Personalized Recommendation Systems In E-Commerce (1st ed., pp. 116-122). Noble Science Press. https://doi.org/10.52458/9788197112492.nsp.2024.eb.ch-11
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