Smartai Cart: a Novel Adaptive Dual-Filter Algorithm Based E-Commerce Platform for Personalized Electronics Shopping

SmartAI Cart is a next-generation E-commerce platform optimized for the electronics domain, featuring a novel hybrid filtering mechanism known as the Adaptive Dual-Filter Algorithm (ADFA). The algorithm enhances product discovery and personalization through a two-layered filtering process: a global filtering layer that applies catalog-wide constraints such as price, brand, and availability before query execution, and a local filtering layer that dynamically adjusts results in realtime based on user interaction history and browsing behavior. This structure significantly improves the relevance of search outcomes while reducing latency. The platform supports a wide range of electronic products and incorporates a responsive, mobile-friendly user interface with multilanguage capabilities and diverse payment options, including EMI and digital wallets. Robust security measures are implemented through blockchainbacked transaction logging and end-to-end encryption, ensuring tamper-proof data handling and protection against fraud. Realtime order tracking is also integrated, enhancing post-purchase transparency and customer satisfaction. SmartAI Cart promotes sustainability by encouraging ecofriendly packaging and listing energy-efficient products. Its modular, three-tier architecture consisting of presentation, application, and data layers-provides scalability, maintainability, and high performance, especially during peak shopping periods. By combining intelligent filtering, secure transaction mechanisms, and user-centric design, the platform addresses key limitations in traditional E-commerce systems. The proposed system lays a strong foundation for future innovations in personalized and sustainable online shopping, particularly within the electronics retail space. AI plays a critical role in SmartAI Cart by enabling pattern recognition, user behavior modeling, and real-time decision-making. Techniques such as decision trees, deep neural networks, and collaborative filtering are employed to anticipate user needs and personalize recommendations. These models continuously learn and adapt through user feedback loops and event tracking.