Research on the effect model of immersive design in interactive advertising
Introduction: The impact of immersive design in interactive advertising focuses on how visuals in Virtual Reality (VR) and interactive elements influence user engagement. With the growing adoption of immersive technologies, advertisers seek innovative ways to enhance ad performance and optimize user...
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| Vydané v: | Salud, Ciencia y Tecnología - Serie de Conferencias Ročník 4; číslo 4; s. 1499 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
06.03.2025
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| Predmet: | |
| ISSN: | 2953-4860 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Introduction: The impact of immersive design in interactive advertising focuses on how visuals in Virtual Reality (VR) and interactive elements influence user engagement. With the growing adoption of immersive technologies, advertisers seek innovative ways to enhance ad performance and optimize user interaction. A high grade of Deep Learning (DL) was used towards the enhancement and prediction of advertisement (ad) performances yet the limitations include insufficient consideration of dynamic user behavior, challenges in analyzing complex multi-modal ad content, and difficulties in generalizing findings across diverse ad formats and user demographics. Objective: The research aims to develop a model that analyzes and predicts the impact of immersive design elements in VR interactive advertising on user engagement. The goal is to leverage innovative DL approaches to focalizing advertising efficiency and improving results of interactions with users. Method: A novel Momentum Search-Driven Intelligent ResNet Architecture (MS-IRA) is proposed, combining an enhanced ResNet model with momentum-based optimization techniques. The dataset comprises engagement measurements, including clicks, time spent, and conversion rates throughout multiple types of ads, as well as interactive and visual ads. Through enhanced persistent associations, the IRA technique improves the feature extraction, and preprocessing enabling it possible to recognize complicated trends in immersive VR ads. Furthermore, by adjusting parameters, MS accelerates up training, assuring faster convergence and improved modeling correctness.Result: The suggested MS-IRA approach accurately facilitates the optimization of immersive advertising designs and improves the interactive advertising efficiency and user interaction results by improving the convergence and model accuracy (AUC with 0.98) in predicting user engagement and ad effectiveness. Conclusion: By leveraging DL techniques, the research offers valuable insights into immersive design strategies, contributing to the evolution of interactive advertising and user-centered engagement approaches. |
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| ISSN: | 2953-4860 |
| DOI: | 10.56294/sctconf20251499 |