AN INTELLIGENT PERSONAL PORTFOLIO WEBSITE WITH LSTM-BASED USER INTERACTION OPTIMIZATION

Abstract [English]

The Personal Portfolio Website is a dynamic digital platform designed to represent an individual's professional profile, integrating intelligent features to enhance user interaction and personalization. By leveraging Long Short-Term Memory (LSTM) networks, the system introduces adaptive elements such as behavior-based content suggestions, personalized project highlights, and user engagement predictions. This adds an intelligent layer to traditional portfolio structures, enhancing the experience for both the website owner and its visitors.
The website is organized into core sections—Home, About Me, Projects, Skills, and Contact—each crafted to deliver a cohesive narrative of the individual’s professional journey. LSTM is utilized to analyze visitor interaction patterns (e.g., frequent scrolls, clicks, and time spent) and adjust content visibility accordingly. For instance, frequently visited project types can be dynamically highlighted, and skill proficiency graphs may be adapted to user preferences.
Built using HTML, CSS, JavaScript, and integrated with Python-based LSTM back-end modules, the website supports responsive design for optimal viewing across various devices. The LSTM model processes interaction sequences in real time to refine the interface and suggest content based on learned patterns. This not only showcases the individual's web development capabilities but also demonstrates practical application of machine learning in enhancing user-centric design.
This project underlines the growing importance of intelligent systems in web development, blending technical proficiency, personal branding, and machine learning to create a responsive and engaging professional portfolio experience.