AI in Digital Forensics: Detecting Deepfakes and Synthetic Media Attacks

Authors- Sowmya .G

Abstract--The evolution of artificial intelligence (AI) has radically transformed numerous industries, with retail emerging as one of the most dynamic beneficiaries. In an era where customer experience is pivotal, traditional recommendation systems are rapidly being replaced by smarter, context-aware engines capable of interpreting and predicting consumer behavior with higher accuracy. This paper explores the design, development, and deployment of context-aware recommendation engines within the retail sector. These advanced systems integrate real-time data streams, including customer demographics, location, shopping history, emotional cues, and even environmental contexts like time, weather, and trending events to generate hyper-personalized shopping suggestions. Unlike traditional algorithms that focus merely on collaborative or content-based filtering, context-aware engines analyze a multidimensional layer of inputs, thereby enriching the customer journey and enhancing satisfaction rates. By employing machine learning (ML) models, natural language processing (NLP), and deep learning frameworks, retailers are empowered to anticipate needs, optimize product placement, and provide intelligent assistance throughout the shopping cycle.The aim of this paper is to provide a comprehensive study on the impact and workings of AI-driven context-aware recommendation systems in transforming next-generation retail environments. It investigates current innovations, deployment challenges, success metrics, and future pathways. It also examines real-world use cases and the measurable ROI for businesses leveraging these systems. In doing so, it contributes to the growing discourse on intelligent commerce and consumer-centric digital transformation. The paper argues that context-aware AI not only augments the personalization landscape but also brings operational efficiency and competitive advantage to retail enterprises.

DOI: /10.61463/ijset.vol.13.issue2.390