Accelerating Real-Time Intent Discovery in Digital Advertising via High-Throughput Systems Integrating Financial-Grade Time Series Forecasting and Large Language Models
Abstract
The efficacy of modern digital advertising infrastructures is increasingly dependent on the ability to perform sub-millisecond intent discovery within highly volatile auction environments. While traditional programmatic architectures rely on historical behavioral heuristics, they often fail to capture the transient, context-dependent shifts in consumer intent that drive conversion. This paper proposes a novel system architecture that integrates financial-grade time series forecasting with large language models to facilitate real-time intent discovery. By treating user interaction streams as high-frequency financial assets, we apply temporal learning pipelines designed for market microstructure to the advertising bidding process. This integration allows for the synthesis of quantitative engagement dynamics with the deep semantic reasoning of transformer-based architectures. Our research focuses on the system-level trade-offs between inferential depth and execution latency, emphasizing the necessity of hardware-aware distributed orchestration. We explore the structural requirements for a high-throughput pipeline that can ingest billions of events daily while maintaining causal consistency across geographically disparate edge nodes. Furthermore, the paper addresses critical socio-technical dimensions, including the governance of autonomous bidding agents, the sustainability of massive-scale transformer inference, and the ethical imperatives of algorithmic fairness in digital commerce. By aligning the precision of financial engineering with the contextual awareness of natural language processing, this framework provides a robust blueprint for the next generation of digital advertising infrastructures, ensuring that intent discovery is both statistically accurate and semantically grounded.
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