Models and Algorithms for Identifying Important Pages Based on User Behavior Analysis on Websites

This article investigates modern models and algorithms for identifying important web pages based on the analysis of user behavior on websites. The study employs Web Usage Mining techniques, user session analysis, page transition probabilities, and clustering methods. The proposed model analyzes users’ navigational activities within a website and enables the identification of the most important pages. The model integrates Markov Chain, PageRank, K-means, and DBSCAN algorithms. Experimental results demonstrated high efficiency in identifying important pages based on user behavior analysis.

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