KNN-Based Machine Learning Techniques to Predict Website Hits and Increase Internal Page Traffic Through User Behavior Analysis

Abstract

In today’s online world, website generate large amount of user interaction, data , including click stream, browsing patterns, and engagement metrics. Proper analysis of this information is essential for predicting website visits and improving internal pages navigation. Machine learning techniques helps automate the process of analyzing user behavior and discovering browsing patterns. One widely used most commonly used method is the K-Nearest Neighbor (KNN) algorithm, known for its simplicity and effectiveness in similarity-based prediction. KNN compares a current user session with and finds the most similar patterns to estimate future page visits and traffic distribution. This review paper explains how KNN models can be used to predict website hits and increase internal page traffic. It discusses web usage mining, feature selection, prediction processes, recommendation method, advantages, Limitation, and possible improvements. The study shows that KNN-based system can improve personalization, reduce bounce rate, and enhance website organization when used with suitable preprocessing and hybrid approaches. Existing work: KNN-based ML techniques analyze user browsing behavior and to predict website visits, recommend pages, improves personalization, reduce bounce rate, & increase engagement.

Introduction

With the growth of online platforms, understanding user behavior on websites is crucial for businesses, educational portals, and digital services. Traditional analytics describe past trends but cannot predict future user actions. Machine Learning (ML) techniques, particularly the K-Nearest Neighbor (KNN) algorithm, address this by analyzing historical session data to forecast future visits, next-page navigation, and page popularity. This enables personalized recommendations, optimized internal linking, improved navigation, reduced bounce rates, and enhanced user engagement.

Web Usage Mining and User Behavior Analysis involve collecting raw data from server logs, cookies, and click streams, preprocessing it to remove noise, identifying sessions, and converting activities into structured features like page sequences, time spent, and visit frequency. ML models then cluster similar sessions to improve prediction accuracy and personalization.

KNN in Website Prediction works by comparing a new session with historical sessions using distance metrics (Euclidean, Manhattan, Cosine) to find the K most similar sessions. Predictions are made based on outcomes of these nearest neighbors, making KNN suitable for navigation prediction, page popularity estimation, and web recommendation tasks.

Literature Review highlights various studies:

• SEO strategies like link building vs. social sharing and multi-category page effects on Google ranking.
• ML applications for web page classification and review rating prediction.
• Predictive models using XGBoost and LightGBM for specific industry web page ranking, showing high accuracy but limited generalizability.

Overall, integrating KNN and ML into web analytics allows dynamic prediction of user behavior, supporting smarter website optimization and personalized user experiences.

Conclusion

In Conclusion, predicting website hits and increasing internal page traffic has become an important task for improving website performance and user engagement. By analyzing user behavior and browsing patterns, it is possible to understand how users interact with different web pages. Machine learning techniques, especially the K-Nearest Neighbor Algorithm, can effectively identify similarities between user session data and web log analysis to recommended relevant internal pages to users. This helps improve navigation, increases pages visits, and enhance the overall user experience. Therefore, applying KNN-based prediction models can support better website management and contribute to higher internal traffic and user satisfaction.

References

[1] Sonya Zhanga and Neal Cabageb , Search Engine Optimization: Comparison of Link Building and Social Sharing, / Journal of Computer Information System 2017, VOL. 57, NO. 2, 148–159 https://doi.org/10.1080/08874417.2016.1183447 [2] Mohamed D. Almadhoun and Nurul Hashimah Ahamed, Hassain Malim, Effects of using multi-category web pages on rank estimation of Google search engine results page, Web Intelligence 2025, Vol. 23(1) 39–55 ©2025– IOS 10.3233/web-230239\\ [3] Goran Matoševi´c , Jasminka Dobša and Dunja Mladeni´c , Using Machine Learning for Web Page Classification in Search Engine Optimization, Future Internet, Volume 13, Issue 1, in 2021 https://www.researchgate.net/publication/348201897_Using_Machine_Learning_for_W eb_Page_Classification_in_Search_Engine_Optimization [4] A.J.W. de Vink1, Natalia Amat-Lefort1, Lifeng Han , Review Graph:A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features, arXiv 2508.13953 (Year: 2025) August 2025 https://doi.org/10.48550/arXiv.2508.13953 [5] joni Salminen, Juan Corporan Using Machine Learning to Predict Ranking of Webpages in the Gift Industry: Factors for Search Engine Optimization, Conference Paper • March 2019 DOI: 10.1145/3361570.3361578 10.1145/3361570.3361578 [6] Mayur Shah & Anmol Valecha, Fake review Detection System using Machine learning, Journal –International Journal of Scientific Research & Engineering DevelopmentVolume-4 Issue 5 Year 2021 https://ijsred.com/volume4/issue5/IJSRED- V4I5P81.pdf?utm_source=chatgpt.com [7] Mayur Shah & Anmol Valecha, Fake review Detection System using Machine learning, Journal –International Journal of Scientific Research & Engineering DevelopmentVolume-4 Issue 5 Year 2021 https://ijsred.com/volume4/issue5/IJSRED- V4I5P81.pdf?utm_source=chatgpt.com [8] K R Lalitha1, Prof. Dr. T. Tripura Sundari, Adoption of Local Search Engine Optimization (SEO) among Small-Scale Entrepreneurs in Chennai: Thematic Qualitative Analysis of Expert Insights, Journal of Information Systems Engineering and Management Vol. 10, No. 43s year 2025 https://doi.org/10.52783/jisem.v10i43s.8515 [9] Firas ALMUKHTAR, Nawzad MAHMOODD, Shahab KAREEM, Search Engine Optimization: A Review, Applied Computer Science, vol. 17,Year 2021 https://doi.org/10.23743/acs-2021-07 [10] Sahana R MBA Student BNMIT, Bangalore, A Study on Effective On-Page and Off-Page SEO Strategies for Building Brand Visibility with Reference to Homilies, SSRN working paper, year 2025 https://ssrn.com/abstract=5476406 [11] Dina Maher Saad, Hayam Mousa, Khalid Amin, Search Engine Optimization Metric, International Journal of Computer Trends and Technology Volume 70 Issue 9, 13-32, September 2022 https://ijcttjournal.org/2022/Volume-70%20Issue-9/IJCTT- V70I9P103.pdf?utm_source=chatgpt.com

Copyright

Copyright © 2026 Swati Sain, Vikas Malik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.