Ingeniería en Sistemas, Electrónica e Industrial

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Author: search.filters.author.Ibarra Lucas, Jorge LuisSubject: search.filters.subject.COMPRAS ONLINE

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    Motor de sugerencias aplicando web scraping para la toma de decisión en la compra de calzado en la línea deportiva
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Tecnologías de la Información, 2024-02) Ibarra Lucas, Jorge Luis; Nogales Portero, Rubén Eduardo
    Online footwear purchases are often challenged by the vast array of brands and designs, posing a difficulty for consumers in making decisions. The inability to physically try on shoes prior to purchasing leaves buyers reliant on information provided by manufacturers and reviews, which may be inaccurate or incomplete. This reliance on potentially unreliable sources makes informed decision-making even more challenging, leading consumers to invest time and effort in comparing models and brands, generating frustration and discouragement. To enhance the shopping experience, this project presents a comprehensive solution with Web Scraping techniques using Scrapy and Selenium, along with a content-based recommendation system, aiming to improve the decision-making process in the purchase of athletic footwear and save search time. The recommendation engine leverages information gathered through Web Scraping on pages of various brands, eliminating the need for exhaustive product searches. These data feed into a content-based recommendation system implemented in Flask, acting as a Web Server Gateway Interface (WSGI) server. Additionally, an interactive user interface is implemented using the ReactJs framework, providing users with the ability to intuitively view product recommendations. These recommendations are generated based on their individual preferences and previously selected products. The results obtained from this implementation reveal a significant improvement in the decision-making process for users, simplifying the search and providing personalized recommendations that align with their individual preferences.