The Optimization of Advertising Content and Prediction of Consumer Response Rate Based on Generative Adversarial Networks

Journal of Organizational and End User Computing, Год журнала: 2025, Номер 37(1), С. 1 - 30

Опубликована: Март 22, 2025

In digital advertising, accurately capturing consumer preferences and generating engaging, personalized content are essential for effective ad optimization. However, traditional methods often rely on single-modal data or static models, limiting their adaptability to dynamic behavior complex, multi-dimensional preferences. To address these challenges, we propose a Multi-modal Adaptive Generative Adversarial Network Ad Optimization Response Prediction (MAGAN-ORP). MAGAN-ORP integrates multi-modal data—including text, image, behavioral features—into unified framework, enabling comprehensive understanding of The model includes an adaptive feedback mechanism that dynamically refines based real-time interactions, ensuring relevancy in evolving environments. Additionally, response prediction module anticipates user engagement, allowing proactive optimization strategies.

Язык: Английский

Enhancements to Large Language Models: Introducing Dynamic Syntactic Insertion for Improved Model Robustness and Generalization

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Окт. 14, 2024

The growing complexity and scale of modern deep learning models have improved the ability to generate understand human language, yet challenges persist in achieving robust generalization syntactic flexibility.Dynamic Syntactic Insertion (DSI) addresses these limitations through novel introduction random variations during finetuning phase, enhancing model's capacity process diverse linguistic structures.Through empirical experiments on GPT-NeoX architecture, significant performance improvements were observed across multiple metrics, including robustness, fluency, accuracy.The DSI-enhanced model consistently outperformed baseline, particularly handling syntactically complex perturbed datasets, demonstrating its adaptability a broader range inputs.Furthermore, incorporation variability led reductions perplexity increased tasks GLUE benchmark, highlighting method's effectiveness.The findings from this study suggest that augmentation techniques, such as DSI, provide promising pathway for improving resilience language environments.

Язык: Английский

Probabilistic Neural Interactions for Dynamic Context Understanding in Large Language Models

Опубликована: Ноя. 18, 2024

The exponential growth in data complexity and volume requires the development of more sophisticated language models capable understanding generating human-like text. Introducing Probabilistic Neural Interactions (PNI) offers a novel approach that enhances dynamic context comprehension through probabilistic mechanisms within neural architectures. This study presents integration PNI into an open-source large model, detailing implementation framework mathematical formulations. Experimental evaluations demonstrate significant improvements model performance metrics, including accuracy adaptability, when compared to baseline models. Additionally, PNI-enhanced exhibits robustness noisy inputs scalability across various sizes, albeit with increased computational resource requirements. These findings suggest contributes advancement models, facilitating complex contextually appropriate processing capabilities.

Язык: Английский