ADAPTIVE AD PLACEMENT USING MULTIMODAL CONTEXTUAL AI FOR LIVE STREAMING

Abstract

The growth rate of the usage of digital platforms to stream live content has raised an urgent demand to have context and adaptive placement of advertisements. The classic systems of delivering the advertisements are not dynamic enough to be able to reflect the dynamic nature of the live environment, in which the focus of the user, the semantics of the content, and the tone of emotion may change in the very moment. The present paper examines how the contextual Artificial Intelligence (AI) of multimodal, i.e., audio, video, textual, and behavioral data, can be used to personalize and adapt to ad placements in live streaming conditions. Multimodal AI systems dynamically evaluate the context of the streaming content by combining machine learning, deep learning, and real-time decision engines to present the most appropriate ad content at the most appropriate time. The study explores the design of architecture, strategies of models, data streams, assessment models, and the future. It also manages such challenges as data fusion, real-time limitations, and ethical matters. This paper highlights the revolutionary nature of multimodal AI to transform online advertising and increase the enrichment of the viewer experience in live media.