An Effective Clustering-Based Web Page Recommendation Framework for E-Commerce Websites
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Publisher
Springer Science and Business Media LLC
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https://link.springer.com/content/pdf/10.1007/s42979-021-00736-z.pdf
Reference72 articles.
1. Adeniyi DA, Wei Z, Yongquan Y. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Appl Comput Inform. 2016;12(1):90–108.
2. Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng. 2005;17(6):734–49.
3. Avazpour I, Pitakrat T, Grunske, L, Grundy J. Dimensions and metrics for evaluating recommendation systems. In: Recommendation systems in software engineering. Berlin: Springer; 2014. pp. 245–73.
4. Baraglia R, Silvestri F. Dynamic personalization of web sites without user intervention. Commun ACM. 2007;50(2):63–7.
5. Bharti PM, Raval TJ. Improving web page access prediction using web usage mining and web content mining. In: 2019 third international conference on electronics, communication and aerospace technology (ICECA); 2019. pp. 1268–73.
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