<![CDATA[The rapid expansion of digital libraries necessitates efficient recommendation systems to help users discover relevant e-books. This study presents the implementation of a simple web-based e-book recommendation system using content-based filtering, developed with the Next.js framework to enhance web loading speed and performance. The system analyzes book metadata and textual content to generate personalized recommendations based on user preferences. Core functionalities include a responsive user interface, book similarity calculations using and cosine similarity, and real-time dynamic suggestions. By leveraging Next.js, the system benefits from server-side rendering (SSR) and static site generation (SSG), ensuring faster page loads and improved user experience. Experimental results indicate that content-based filtering effectively suggests relevant e-books but faces challenges such as the cold-start problem. Future work may integrate hybrid filtering techniques to improve recommendation accuracy and user engagement.]]>
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