Abstract
The rapid growth of e-commerce platforms has resulted in an overwhelming number of product choices, often leaving customers confused when selecting the most suitable device. Traditional recommendation systems rely on collaborative or content-based filtering but lack conversational interactivity and real-time personalization. This project proposes an Electronics Recommendation Assistant built using Flask framework, Gemini AI, and a local product dataset to provide intelligent, interactive product guidance. The system enables users to converse naturally with the chatbot to specify their preferences based on product type (laptop/phone), user designation (student, gamer, developer, professional, designer), and budget constraints. The chatbot filters products from a structured dataset (student.json), dynamically displays product images, and suggests the best option under the given budget. Additionally, a secure MySQL-based authentication module is integrated for user login and registration. By combining conversational AI with structured recommendation rules, the system enhances user experience, reduces decision fatigue, and demonstrates the practical application of generative AI in e-commerce personalization.
Keywords
Artificial Intelligence, Product Recommendation System, Conversational AI, Google Gemini API, Flask Web Application, Streamlit, User Authentication, Recommender Systems, Chatbot, MySQL Database, Electronics Advisor, Human–Computer Interaction


Introduction
In the digital era, the availability of a vast array of electronic products such as laptops and smartphones has transformed the way users search, compare, and purchase devices. Online platforms like Amazon, Flipkart, and similar e-commerce portals present thousands of options across varying specifications, budgets, and use cases. While this abundance of choice empowers consumers, it also creates a paradox of choice, where users often struggle to make an informed and confident decision. Conventional search and filter mechanisms on e-commerce websites demand that users manually select specifications such as RAM, processor, screen size, or price range, which can be overwhelming for individuals who are not technically proficient. This creates a gap between product availability and user accessibility, leading to decision fatigue and potential dissatisfaction with purchases.
To bridge this gap, AI-driven recommender systems have emerged as powerful tools for personalized product suggestions. These systems analyze user behavior, preferences, and contextual information to generate recommendations that are more relevant than manual searches. However, many existing recommendation systems operate in the background and provide suggestions in a non-interactive format, often based solely on previous browsing or purchase history. This limits user engagement and fails to address situations where a user’s current need does not align with their historical data. To overcome this limitation, conversational recommender systems have been introduced, combining the power of dialogue-based interaction with product filtering logic. By simulating natural conversation, these systems allow users to express their requirements in everyday language, thereby simplifying the decision-making process.
Recent advances in Generative Artificial Intelligence (GenAI) have further strengthened the capabilities of conversational systems. Models such as Google’s Gemini demonstrate state-of-the-art performance in natural language understanding and generation, enabling chatbots to engage in human-like conversations. Unlike rule-based chatbots, generative models can understand contextual nuances, handle open-ended queries, and provide adaptive responses. This flexibility makes them highly suitable for product recommendation scenarios, where user requirements are diverse and dynamic. Integrating generative AI into a recommendation system allows not only structured product guidance but also the ability to engage in casual conversation, enhancing user trust and satisfaction.

Block Diagram