Racket Guru: a personalized racket recommendation using content-based filtering

The Racket Guru system addresses a well-known issue that beginner and intermediate badminton players have when they cannot find the right racket because of the lack of expertise in the store and generic recommendations online. To address this issue, the system aims at creating a more intelligent and easier to use system that will provide the personalized guidance that matches the skill level, playing style, and personal preferences of the player. To this end, it implements a Content-Based Filtering system where the user inputs are compared to a selected database of racket specifications to produce personalized recommendations. It also includes sophisticated search and filtering, side by side comparison of rackets, and product description aimed at making informed decisions. It was developed using the Iterative Waterfall Model that implied thorough analysis of requirements, designing, implementation, and usability testing using the System Usability Scale (SUS). The outcome is a web-based application that has a clean responsive interface that makes it easy to interact with. The results of usability testing show a great level of user satisfaction and better confidence in making decisions. The key differentiators of the project are that it combines content-based recommendation methods with interactive features like questionnaires, comparison features and detailed product descriptions, which are not present on most commercial websites. Finally, the case of Racket Guru shows that smarter, people-focused design can reduce decision fatigue, increase player engagement and help beginner and intermediate players make the right choice about rackets, thus promoting the expansion and accessibility of the badminton community.