TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping

Ilustraciones a color, tablas

Autores:: Marín Moreno, Miguel Ángel
Restrepo Martínez, Santiago

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2025

Institución:: Universidad de San Buenaventura

Repositorio:: Repositorio USB

Idioma:: spa

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dc.title.spa.fl_str_mv	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping
title	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping
spellingShingle	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping Aplicaciones informáticas Aplicaciones multimedia Aplicaciones web 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Machine learning Web scraping Recomendaciones personalizadas
title_short	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping
title_full	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping
title_fullStr	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping
title_full_unstemmed	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping
title_sort	TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping
dc.creator.fl_str_mv	Marín Moreno, Miguel Ángel Restrepo Martínez, Santiago
dc.contributor.advisor.none.fl_str_mv	Dinas, Simena Marin Montealegre, Kelly Daniella
dc.contributor.author.none.fl_str_mv	Marín Moreno, Miguel Ángel Restrepo Martínez, Santiago
dc.contributor.jury.none.fl_str_mv	Hidalgo Suárez, Carlos Giovanny
dc.contributor.researchgroup.none.fl_str_mv	Grupo de Investigación Laboratorio de Investigación para el Desarrollo de la Ingeniería de Software (LIDIS) (Cali)
dc.subject.armarc.none.fl_str_mv	Aplicaciones informáticas Aplicaciones multimedia Aplicaciones web
topic	Aplicaciones informáticas Aplicaciones multimedia Aplicaciones web 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Machine learning Web scraping Recomendaciones personalizadas
dc.subject.ddc.none.fl_str_mv	000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
dc.subject.proposal.eng.fl_str_mv	Machine learning Web scraping
dc.subject.proposal.spa.fl_str_mv	Recomendaciones personalizadas
description	Ilustraciones a color, tablas
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-07-14T17:12:55Z
dc.date.available.none.fl_str_mv	2025-07-14T17:12:55Z
dc.date.issued.none.fl_str_mv	2025
dc.type.none.fl_str_mv	Trabajo de grado - Pregrado
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dc.identifier.citation.none.fl_str_mv	Restrepo, S, Marín, M, (2025) “TuMI: Desarrollo de una Herramienta Prototipo para la Recomendación Personalizada de Motocicletas Usando Machine Learning y Web Scrapping”. Trabajo de grado Ingeniería de Sistemas, Universidad de San Buenaventura Cali, Facultad de Ingeniería, 2025
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/10819/25458
identifier_str_mv	Restrepo, S, Marín, M, (2025) “TuMI: Desarrollo de una Herramienta Prototipo para la Recomendación Personalizada de Motocicletas Usando Machine Learning y Web Scrapping”. Trabajo de grado Ingeniería de Sistemas, Universidad de San Buenaventura Cali, Facultad de Ingeniería, 2025
url	https://hdl.handle.net/10819/25458
dc.language.iso.none.fl_str_mv	spa
language	spa
dc.relation.references.none.fl_str_mv	"Best-Selling Products in Colombia 2024-2025," Americas Market Intelligence, Sept. 1, 2024. "Motorcycles - Colombia Market Forecast," Statista, Accessed on 2024. "Colombian Motorcycles Market in 2024 Grows 8.2% in The First Half," MotorCyclesData, Aug. 19, 2024. A. Joshi et al., "A Machine Learning Based Bike Recommendation System Catering To Users Travel Needs," IEEE Xplore, Feb. 2020. J. Ren, Y. Li, J. Zhou, et al., "Developing machine learning models for personalized treatment strategies," Scientific Reports, vol. 14, no. 7, May 2024. S. Chen, X. Zhang, "A comparative study on machine learning models for recommendation systems," Journal of Data Science and Analytics, vol. 10, no. 2, pp. 150-162, April 2023. A. Joshi et al., "A Machine Learning Based Bike Recommendation System Catering To Users Travel Needs," ResearchGate, Feb. 2020. M. Smith, "Bayesian models in recommender systems: An overview," Journal of Computational Intelligence, vol. 12, no. 3, pp. 100-112, March 2022. 6WResearch, "Colombia Motorcycle Accessories Market (2024-2030)," Statista, "Motorcycle industry in Colombia - Statistics & Facts," Recommender Systems: Why the Future is Real-Time Machine Learning, "RT Insights," 6WResearch, "Colombia Motorcycle Accessories Market (2024-2030)," Statista, "Motorcycle industry in Colombia - Statistics & Facts," Recommender Systems: Why the Future is Real-Time Machine Learning, "RT Insights," Vehicle Recommendation System using Hybrid Techniques, "IEEE Xplore," AI In The Motorcycle Dealer Industry, "Artificial Intelligence in the Motorcycle Industry," FasterCapital, "Revolutionizing Motorcycle Maintenance: Predictive Analytics and Machine Learning," Recommender Systems: Why the Future is Real-Time Machine Learning, "RT Insights," R. Smith y A. Gonzales, "Recomendaciones personalizadas en comercio electrónico usando machine learning," Journal of Data Science, vol. 15, pp. 102-117, 2020. FasterCapital, "Revolutionizing Motorcycle Maintenance: Predictive Analytics and Machine Learning," Vehicle Recommendation System using Hybrid Techniques, "IEEE Xplore," I. Naing, S.T. Aung, K.H. Wai, N. Funabiki, "A Reference Paper Collection System Using Web Scraping," Electronics, vol. 13, no. 14, 2024. A. Tzana, "Real estate property comparison in the Greek market using advanced image similarity methods and web scraping techniques," Y. Zhao, J. Zhao, E.Y. Lam, "House price prediction: A multi-source data fusion perspective," Big Data Mining and Analytics, 2024. S. Kumar, L. Gupta, "Machine Learning Models for Personalized Recommendations: A Survey," ACM Transactions on Information Systems, vol. 39, no. 4, 2024. B. Zhang, X. Liu, "Building Scalable Architectures for E-Commerce Recommendation Systems," IEEE Transactions on Cloud Computing, vol. 12, no. 3, 2024. R. Smith, A. Hernandez, "UX Principles for AI-Driven User Interfaces: A Practical Guide," Design and AI Journal, vol. 2, no. 1, 2023. P. Goncalves, M. Alves, "Creating Seamless UI Integration with AI-Backed Recommendation Engines," International Journal of Human-Computer Interaction, vol. 15, no. 4, pp. 456-470, 2023. M. Garcia, F. Lopez, "Machine Learning Models in Rural Markets: The Case of Vehicle Recommenders," Rural Computing Journal, vol. 11, no. 3, pp. 210-225, 2024. J. Turing, "Personalization Techniques in Online Markets: Integrating ML with Consumer Preferences," E-Commerce Systems Journal, vol. 6, no. 2, pp. 98-112, 2023. A. Perez, L. Johnson, "Coordinating Multidisciplinary Teams for AI Projects: Case Studies in Recommender Systems," IEEE AI Conference Proceedings, 2024. M. Wilson, K. Clark, "Long-Term Benefits of Personalized Recommendation Systems in Automotive Retail," Journal of Retail Innovations, vol. 9, no. 3, 2023. F. Saini, V. Bhatia, "AI-Driven Loyalty Programs: Enhancing Customer Experience through Personalized Offers," Customer Engagement and Loyalty Journal, vol. 13, no. 1, pp. 15-30, 2024. S. D. Bhopale, A. Sahu, and K. Pandyaji, “Web Services Recommendation system using Machine Learning Algorithms,” in 2023 4th International Conference for Emerging Technology (INCET), 2023, pp. 1-7. A. K. Dey, V. K. Chauhan, P. K. Singh, and P. Choudhury, “LSTM-Based Top N Recommendation System using Cognitive Data,” in 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), 2022, pp. 93-98. J. Foerderer, “Should we trust web-scraped data?,” ArXiv, vol. abs/2308.02231, 2023. S. Kumar, M. M. Nasralla, I. García-Magariño, and H. Kumar, “A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics,” PeerJ Computer Science, vol. 7, 2021. S. D. S. Sirisuriya, “Importance of Web Scraping as a Data Source for Machine Learning Algorithms - Review,” in 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), 2023, pp. 134-139. L. K. H., “Movie Recommendations Based on Emotions Using Web Scraping,” International Journal for Research in Applied Science and Engineering Technology, 2022. Y. Liu, M. Liao, and J. Wang, “Exploring Graph Neural Networks for Improved Collaborative Filtering,” Journal of Artificial Intelligence Research, vol. 74, 2020, pp. 1-19. D. R, P. J. P., and S. M. A., “Performance Analysis of Machine Learning - Semantic Relational Approach based Job Recommendation System,” in 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), 2023, pp. 1478-1486. X. Feng, J. Hu, and X. Zhu, “Machine Learning Based Personalized Movie Research and Implementation of Recommendation System,” in 2022 International Conference on Culture-Oriented Science and Technology (CoST), 2022, pp. 74-78. A. Nair, C. Paralkar, J. Pandya, Y. Chopra, and D. Krishnan, “Comparative Review on Sentiment analysis-based Recommendation system,” in 2021 6th International Conference for Convergence in Technology (I2CT), 2021, pp. 1-6. Y. Fang, “Research on Personalized Recommendation System Based on Machine Learning,” in 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), 2022, pp. 1209-1213. S. Gasmi, T. Bouhadada, and A. Benmachiche, “Survey on Recommendation Systems,” Proceedings of the 10th International Conference on Information Systems and Technologies, 2020. G. Pang, X. Wang, L. Wang, F. Hao, Y. Lin, P. Wan, and G. Min, “Efficient Deep Reinforcement Learning-Enabled Recommendation,” IEEE Transactions on Network Science and Engineering, vol. 10, pp. 871-886, 2023. M. Delianidi, M. Salampasis, K. Diamantaras, T. Siomos, A. Katsalis, and I. Karaveli, “A Graph-based Method for Session-based Recommendations,” in 24th Pan-Hellenic Conference on Informatics, 2020. “Popularity Based Recommendation System,” International Journal of Engineering and Advanced Technology, vol. 9, no. 6, 2020. M. Özkara and M. Turan, “Personalized News Recommendation System,” İstanbul Ticaret Üniversitesi Teknoloji ve Uygulamalı Bilimler Dergisi, 2022. E. Zhang, W. Ma, J. Zhang, and X. Xia, “A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning,” Electronics, vol. 12, no. 4, pp. 502-510, 2023. D. Balakrishnan, A. P. Kumar, K. Sai, K. Reddy, R. Kumar, K. Aadith, and S. Madhan, “Agricultural Crop Recommendation System,” in 2023 3rd International Conference on Intelligent Technologies (CONIT), 2023, pp. 217-221. M. Pasha, C. R. S. Rao, A. Geetha, T. F. Fernandez, and Y. K. Bhargavi, “A VOS analysis of LSTM Learners Classification for Recommendation System,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 2, 2023, pp. 342-349. Y. Wang, X. Su, and L. Ma, “A Collaborative Filtering Recommendation System Based on Ensemble Methods,” IEEE Access, vol. 9, 2021, pp. 45112-45121. J. Zhang, X. Wang, and H. Liu, “Learning-Based Recommendation System using Clustering Algorithms,” International Journal of Automation and Computing, vol. 19, no. 1, pp. 121-131, 2022. A. Ali, A. Ibrahim, and N. Hussain, “Sentiment-Aware Recommendation System Using Natural Language Processing,” Applied Sciences, vol. 13, no. 7, pp. 3421-3432, 2023. P. Anand, K. Vignesh, and E. Karthikeyan, “A Hybrid Recommendation System Integrating User Preferences and Data Mining,” International Journal of Computer Applications, vol. 183, no. 22, 2021, pp. 23-31. F. Sadouki and S. Kechid, “A Deep Learning Architecture for Profile Enrichment and Content Recommendation,” in 2020 IEEE International Conference on Smart Data, 2020, pp. 131-141. Y. Liu, M. Liao, and J. Wang, “Exploring Graph Neural Networks for Improved Collaborative Filtering,” Journal of Artificial Intelligence Research, vol. 74, 2020, pp. 1-19. J. Doe, "Traditional vs Agile Methodologies in Software Development," Journal of Software Engineering, vol. 45, no. 3, pp. 23-29, 2022. A. Smith, "The Waterfall Model and Its Limitations in Modern Software Development," International Journal of Information Systems, vol. 34, no. 2, pp. 110-119, 2021. M. Green, "Agile Methodologies and Their Application in Small Teams," Computer Science Review, vol. 27, no. 1, pp. 87-95, 2023. P. White, "Waterfall vs Agile: A Detailed Comparison," Engineering and Management Journal, vol. 39, no. 4, pp. 5-15, 2020. R. Brown, "Scrum: A Practical Guide to Agile Development," Agile Journal, vol. 19, no. 1, pp. 45-56, 2020. E. Adams, "Rapid Application Development (RAD): A Comprehensive Review," Software Development Quarterly, vol. 22, no. 2, pp. 145-158, 2023. T. Lewis, "Iterative Prototyping in RAD for Enhanced User Feedback," Journal of Agile Methods, vol. 31, no. 5, pp. 98-104, 2021.
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spelling	Dinas, Simenavirtual::4251-1Marin Montealegre, Kelly Daniellavirtual::4252-1Marín Moreno, Miguel ÁngelRestrepo Martínez, SantiagoHidalgo Suárez, Carlos Giovannyvirtual::4253-1Grupo de Investigación Laboratorio de Investigación para el Desarrollo de la Ingeniería de Software (LIDIS) (Cali)2025-07-14T17:12:55Z2025-07-14T17:12:55Z2025Ilustraciones a color, tablasDesde el 2020, el mercado de motocicletas en Colombia ha crecido considerablemente, ofreciendo a los compradores una amplia variedad de modelos. Sin embargo, esta diversidad también ha complicado el proceso de selección, especialmente para quienes carecen de experiencia técnica en vehículos. Ante esta necesidad, surge TuMI, Tu Moto Ideal, una aplicación web diseñada para recomendar motocicletas a los usuarios de manera personalizada, basándose en sus preferencias y necesidades específicas. Utilizando técnicas de web scraping para recopilar información actualizada de concesionarios y algoritmos de machine learning, TuMI analiza características clave como presupuesto, tipo de uso y especificaciones técnicas para generar recomendaciones objetivas, facilitando la toma de decisiones y optimizando la experiencia de compra. La herramienta busca ofrecer una solución tecnológica que reduzca el tiempo de búsqueda y mejore la precisión de las decisiones, aportando valor tanto a compradores expertos como inexpertos en un mercado en constante crecimiento.Since 2020, the motorcycle market in Colombia has grown considerably, offering buyers a wide variety of models. However, this diversity has also complicated the selection process, especially for those who lack technical expertise in vehicles. In response to this need, TuMI, a web application designed to recommend motorcycles to users in a personalized way, based on their specific preferences and needs, has emerged. Using web scraping techniques to collect updated information from dealers and machine learning algorithms, TuMI analyzes key characteristics such as budget, type of use and technical specifications to generate objective recommendations, facilitating decision making and optimizing the buying experience. The tool seeks to offer a technological solution that reduces search time and improves decision accuracy, providing value to both expert and inexperienced buyers in a constantly growing market.PregradoIngeniero de SistemasSedes::Caliapplication/pdfRestrepo, S, Marín, M, (2025) “TuMI: Desarrollo de una Herramienta Prototipo para la Recomendación Personalizada de Motocicletas Usando Machine Learning y Web Scrapping”. Trabajo de grado Ingeniería de Sistemas, Universidad de San Buenaventura Cali, Facultad de Ingeniería, 2025https://hdl.handle.net/10819/25458spaUniversidad de San Buenaventura - CaliCaliFacultad de IngenieríaCaliIngeniería de Sistemas"Best-Selling Products in Colombia 2024-2025," Americas Market Intelligence, Sept. 1, 2024."Motorcycles - Colombia Market Forecast," Statista, Accessed on 2024."Colombian Motorcycles Market in 2024 Grows 8.2% in The First Half," MotorCyclesData, Aug. 19, 2024.A. Joshi et al., "A Machine Learning Based Bike Recommendation System Catering To Users Travel Needs," IEEE Xplore, Feb. 2020.J. Ren, Y. Li, J. Zhou, et al., "Developing machine learning models for personalized treatment strategies," Scientific Reports, vol. 14, no. 7, May 2024.S. Chen, X. Zhang, "A comparative study on machine learning models for recommendation systems," Journal of Data Science and Analytics, vol. 10, no. 2, pp. 150-162, April 2023.A. Joshi et al., "A Machine Learning Based Bike Recommendation System Catering To Users Travel Needs," ResearchGate, Feb. 2020.M. Smith, "Bayesian models in recommender systems: An overview," Journal of Computational Intelligence, vol. 12, no. 3, pp. 100-112, March 2022.6WResearch, "Colombia Motorcycle Accessories Market (2024-2030),"Statista, "Motorcycle industry in Colombia - Statistics & Facts,"Recommender Systems: Why the Future is Real-Time Machine Learning, "RT Insights,"6WResearch, "Colombia Motorcycle Accessories Market (2024-2030),"Statista, "Motorcycle industry in Colombia - Statistics & Facts,"Recommender Systems: Why the Future is Real-Time Machine Learning, "RT Insights,"Vehicle Recommendation System using Hybrid Techniques, "IEEE Xplore,"AI In The Motorcycle Dealer Industry, "Artificial Intelligence in the Motorcycle Industry,"FasterCapital, "Revolutionizing Motorcycle Maintenance: Predictive Analytics and Machine Learning,"Recommender Systems: Why the Future is Real-Time Machine Learning, "RT Insights,"R. Smith y A. Gonzales, "Recomendaciones personalizadas en comercio electrónico usando machine learning," Journal of Data Science, vol. 15, pp. 102-117, 2020.FasterCapital, "Revolutionizing Motorcycle Maintenance: Predictive Analytics and Machine Learning,"Vehicle Recommendation System using Hybrid Techniques, "IEEE Xplore,"I. Naing, S.T. Aung, K.H. Wai, N. Funabiki, "A Reference Paper Collection System Using Web Scraping," Electronics, vol. 13, no. 14, 2024.A. Tzana, "Real estate property comparison in the Greek market using advanced image similarity methods and web scraping techniques,"Y. Zhao, J. Zhao, E.Y. Lam, "House price prediction: A multi-source data fusion perspective," Big Data Mining and Analytics, 2024.S. Kumar, L. Gupta, "Machine Learning Models for Personalized Recommendations: A Survey," ACM Transactions on Information Systems, vol. 39, no. 4, 2024.B. Zhang, X. Liu, "Building Scalable Architectures for E-Commerce Recommendation Systems," IEEE Transactions on Cloud Computing, vol. 12, no. 3, 2024.R. Smith, A. Hernandez, "UX Principles for AI-Driven User Interfaces: A Practical Guide," Design and AI Journal, vol. 2, no. 1, 2023.P. Goncalves, M. Alves, "Creating Seamless UI Integration with AI-Backed Recommendation Engines," International Journal of Human-Computer Interaction, vol. 15, no. 4, pp. 456-470, 2023.M. Garcia, F. Lopez, "Machine Learning Models in Rural Markets: The Case of Vehicle Recommenders," Rural Computing Journal, vol. 11, no. 3, pp. 210-225, 2024.J. Turing, "Personalization Techniques in Online Markets: Integrating ML with Consumer Preferences," E-Commerce Systems Journal, vol. 6, no. 2, pp. 98-112, 2023.A. Perez, L. Johnson, "Coordinating Multidisciplinary Teams for AI Projects: Case Studies in Recommender Systems," IEEE AI Conference Proceedings, 2024.M. Wilson, K. Clark, "Long-Term Benefits of Personalized Recommendation Systems in Automotive Retail," Journal of Retail Innovations, vol. 9, no. 3, 2023.F. Saini, V. Bhatia, "AI-Driven Loyalty Programs: Enhancing Customer Experience through Personalized Offers," Customer Engagement and Loyalty Journal, vol. 13, no. 1, pp. 15-30, 2024.S. D. Bhopale, A. Sahu, and K. Pandyaji, “Web Services Recommendation system using Machine Learning Algorithms,” in 2023 4th International Conference for Emerging Technology (INCET), 2023, pp. 1-7.A. K. Dey, V. K. Chauhan, P. K. Singh, and P. Choudhury, “LSTM-Based Top N Recommendation System using Cognitive Data,” in 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), 2022, pp. 93-98.J. Foerderer, “Should we trust web-scraped data?,” ArXiv, vol. abs/2308.02231, 2023.S. Kumar, M. M. Nasralla, I. García-Magariño, and H. Kumar, “A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics,” PeerJ Computer Science, vol. 7, 2021.S. D. S. Sirisuriya, “Importance of Web Scraping as a Data Source for Machine Learning Algorithms - Review,” in 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), 2023, pp. 134-139.L. K. H., “Movie Recommendations Based on Emotions Using Web Scraping,” International Journal for Research in Applied Science and Engineering Technology, 2022.Y. Liu, M. Liao, and J. Wang, “Exploring Graph Neural Networks for Improved Collaborative Filtering,” Journal of Artificial Intelligence Research, vol. 74, 2020, pp. 1-19.D. R, P. J. P., and S. M. A., “Performance Analysis of Machine Learning - Semantic Relational Approach based Job Recommendation System,” in 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), 2023, pp. 1478-1486.X. Feng, J. Hu, and X. Zhu, “Machine Learning Based Personalized Movie Research and Implementation of Recommendation System,” in 2022 International Conference on Culture-Oriented Science and Technology (CoST), 2022, pp. 74-78.A. Nair, C. Paralkar, J. Pandya, Y. Chopra, and D. Krishnan, “Comparative Review on Sentiment analysis-based Recommendation system,” in 2021 6th International Conference for Convergence in Technology (I2CT), 2021, pp. 1-6.Y. Fang, “Research on Personalized Recommendation System Based on Machine Learning,” in 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), 2022, pp. 1209-1213.S. Gasmi, T. Bouhadada, and A. Benmachiche, “Survey on Recommendation Systems,” Proceedings of the 10th International Conference on Information Systems and Technologies, 2020.G. Pang, X. Wang, L. Wang, F. Hao, Y. Lin, P. Wan, and G. Min, “Efficient Deep Reinforcement Learning-Enabled Recommendation,” IEEE Transactions on Network Science and Engineering, vol. 10, pp. 871-886, 2023.M. Delianidi, M. Salampasis, K. Diamantaras, T. Siomos, A. Katsalis, and I. Karaveli, “A Graph-based Method for Session-based Recommendations,” in 24th Pan-Hellenic Conference on Informatics, 2020.“Popularity Based Recommendation System,” International Journal of Engineering and Advanced Technology, vol. 9, no. 6, 2020.M. Özkara and M. Turan, “Personalized News Recommendation System,” İstanbul Ticaret Üniversitesi Teknoloji ve Uygulamalı Bilimler Dergisi, 2022.E. Zhang, W. Ma, J. Zhang, and X. Xia, “A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning,” Electronics, vol. 12, no. 4, pp. 502-510, 2023.D. Balakrishnan, A. P. Kumar, K. Sai, K. Reddy, R. Kumar, K. Aadith, and S. 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White, "Waterfall vs Agile: A Detailed Comparison," Engineering and Management Journal, vol. 39, no. 4, pp. 5-15, 2020.R. Brown, "Scrum: A Practical Guide to Agile Development," Agile Journal, vol. 19, no. 1, pp. 45-56, 2020.E. Adams, "Rapid Application Development (RAD): A Comprehensive Review," Software Development Quarterly, vol. 22, no. 2, pp. 145-158, 2023.T. 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TuMi: desarrollo de una herramienta prototipo para la recomendación personalizada de motocicletas usando machine learning y web scrapping

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