Online Health Community Recommendation System with LDA-FCM Approach

Abstract:

Online health communities generate vast knowledge on various topics, making it challenging to track latent themes and correlations among key variables. Identifying these hidden variables and their relationships can enhance user engagement and content curation. This paper introduces a novel approach using fuzzy cognitive maps to uncover these variables and causal relationships in online health communities, providing users with valuable insights for disease management and treatment. The study employed Latent Dirichlet Allocation (LDA) to generate topics from an online health forum, using this as evidence to construct fuzzy cognitive maps. The proposed system reflects disease development and offers significant insights for managing health conditions. The findings have theoretical and practical implications for developing online recommendation systems in the healthcare domain. This study contributes to the literature by proposing a method for identifying key variables and relationships, aiding health professionals and patients in understanding and managing health conditions. Future research will explore the system’s effectiveness in real-world settings and the role of user-generated content in enhancing recommendation systems.

• Periodical: Engineering Headway (Volume 27)
• Pages: 329-342
• DOI: https://doi.org/10.4028/p-re6F0s
• Online since: October 2025
• Authors: Shan Shan, Umar Farooq, Jia Lou Wang*, Yu Lei Li, Düriye Betül Gümüş
• Keywords: Diabetes, Dirichlet Allocation, Fuzzy Cognitive Map, Online Health Community, Recommendation Systems

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