Hybrid approaches in smart sensing for detecting buying intent: performance, reasoning, and real-world deployment

Abstract Buying intent detection is a central challenge that cuts across several artificial intelligence domains, including natural language processing, knowledge representation, and decision-making systems. Inferring a user’s purchase intent correctly is of critical importance in real-world applications such as e-commerce, customer relationship management, and personalized recommendation systems, where it directly impacts conversion rates, user engagement, and operational efficiency. Over the years, a wide range of approaches have been proposed that span deep-learning models, knowledge-graph based reasoning, reinforcement learning, and more recently agent-based and multi-agent systems. This review systematically analyzes the evolution and efficiency of these paradigms with an eye toward real-world applicability. Deep-learning models based on transformers are among the most powerful detection models, given large labeled datasets, but offer little in terms of interpretability or robustness under domain shift. Methods leveraging knowledge graphs augment reasoning and interpretability by modeling structured relationships but come at significant construction and maintenance costs. Reinforcement learning introduces the ability to learn adaptively in sequential and dynamic environments but is sensitive to reward design and sample efficiency constraints. AI-agent systems provide more comprehensive autonomy and facilitate multi-step coordination tasks while introducing novel challenges related to reliability, latency, and operational overhead. We provide a unified taxonomy that synthesizes these approaches and compare them on several dimensions, including detection accuracy, quality of the induced reasoning, robustness to domain changes, and efficiency in the deployment setting. Unlike previous surveys, this work puts an emphasis on deployment-realistic benchmarks such as latency, cost of inference, quality of the grounded output, and tool success rate, supported by reported benchmarks and ablation studies. We conclude by pointing out open challenges regarding scalability, interpretability, and integration of symbolic and data-driven methods, giving practical insights for both researchers and practitioners while developing intent-aware AI systems.