RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop

RideSmart is a web-based tool designed to provide JavidsonMotorshop clients with personalized product recommendations, including appropriate motorcycle parts, accessories, and services. To create recommendations that are specific to each user, the system examines purchase histories, product qualities, and consumer preferences using TF-IDF (Term Frequency–Inverse Document Frequency) and descriptive analytics. Compared to generic promos, this not only expedites and enhances the shopping experience but also provides insights into consumer behavior, including demand trends, buying habits, and popular products, which aid the store in improving its marketing and stocking plans. The RideSmart recommendation system for JavidsonMotorshop uses TF-IDF to extract key features and keywords from customer reviews and searches, enabling personalized motorcycle product suggestions that improve purchase decisions and customer experience (Huang, 2025). Descriptive analytics complements this by analyzing historical sales, browsing behavior, and product interaction data to identify trends and patterns, supporting data-driven marketing and inventory management (Harvard Business School Online, 2021). Together, these approaches allow the system to tailor recommendations to individual preferences, enhancing sales efficiency and operational effectiveness (Sigma Computing, 2025; MEEGLE, 2025).Laravel, a PHP framework, Python, MySQL, and front-end technologies (HTML, CSS) are used in the technical implementation of RideSmart. Functionality (accurate product details and seamless search, cart, checkout, etc.), reliability (availability and recovery from errors), efficiency (page load speeds, resource usage, handling many users), usability (intuitive navigation, responsive controls, design), and portability (ease of deployment on other servers or in other locales) were the main dimensions that were examined in order to assess its quality using the ISO 25010 standard. Both "technical" and "user" respondent groups evaluated the system: technical respondents emphasized functionality, dependability, and performance, particularly the accuracy and pertinence of recommendations made possible by TF-IDF and analytics, while users prioritized usability, acceptability, and enhancements to day-to-day operations (particularly streamlining product search and enhancing customer satisfaction). The evaluation provides a fair perspective because the respondents are a combination of about 60% users and 40% technical stakeholders. Although there is room for improvement, overall, the results show that RideSmart is technically sound, practically helpful, and effective in real-world situations. Among the suggested enhancements are enhancing and preserving theunderlying data (for both products and users), integrating diagnostic or external tools for more precise compatibility, allowing recommendation adjustments based on a customer’s mechanical expertise, broadening the product database, adding multilingual support, incorporating user feedback into the recommendation loop, and creating a mobile‑friendly version to extend reach and usability.

Recommendation system, PHP, MySQL, ISO 25010

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