Personalized Recommendation Systems for Behavioral Health Interventions Using NLP-Based Chatbots

Xiaonan Qian^*

• School of Foreign Languages, Zhejiang University of Science and Technology, Zhenjiang, China

The irncreasing reliance on digital platforms for mental health support underscores the necessity for advanced personalized recommendation systems in behavioral interventions. Natural language processing-driven chatbots offer an effective medium for real-time interaction, facilitating the delivery of tailored therapeutic guidance. This study addresses the challenges of modeling dynamic and multimodal mental states by advancing beyond traditional systems that depend on static rule-based or shallow learning models. Such conventional approaches often fail to accommodate the evolving complexity of psychological conditions and lack the capability to integrate diverse data modalities, including textual input, sensor data, and self-reports. Their limited interpretability and misalignment with clinical frameworks restrict their practical utility. To overcome these limitations, we propose an intelligent system architecture that combines NeuroChart, a clinical state encoding schema designed to structure and contextualize psychological constructs, and CognitionMap, a latent reasoning module that captures chronological, conceptual, and networked patterns in behavioral dynamics. CognitionMap applies Stein-based variational inference and graph-oriented kernels to model fine-grained mental-affective trajectories at both individual and population levels. CAI (Clinical Alignment Injection) complements this architecture by embedding domain-sensitive priors derived from psychiatric ontology graphs, ensuring that the inference process respects diagnostic hierarchies and treatment protocols. Empirical evaluations demonstrate that our framework significantly outperforms baseline methods in affective state prediction and personalized intervention recommendation, while maintaining clinical interpretability and adaptability. This research presents a technically robust and clinically grounded approach for integrating probabilistic modeling and NLP in behavioral health chatbots, advancing the development of next-generation digital psychotherapy tools.