Enhancing Healthcare Recommendations: A Privacy-Protective and Interpretable Cross-Domain Framework

Xun Liang, Zhiying Li, Hongxun Jiang

Abstract: Cross-domain recommendations in healthcare services differ from traditional ones in electronic commerce due to the need for heightened medical privacy protection for a small group of users, while ensuring the majority, who may lack sufficient medical knowledge, can understand the recommendations. To recommend doctors who provide online consultations to health video viewers and enable multimodal cross-domain recommendations from short video platforms (source domain) to online healthcare communities (target domain), this paper introduces a framework based on the User-Centric Synthetic Data Architect (UCSDA) and Pre-trained Large Language Model (PtLLM). UCSDA employs a user-centric, advanced selection-synthesis mechanism to filter users' cold interaction items and synthesize noise items, reducing privacy leakage risk. PtLLM focuses on necessary patient and doctor IDs during the recommendation decision process to generate explanations. The model's effectiveness and scalability were validated using three public datasets and a healthcare cross-domain recommendation dataset. In addition to traditional evaluation metrics, strong privacy metrics and the unique sentence ratio were used to assess privacy protection and interpretability. We also compared the characteristics of privacy protection and interpretability between e-commerce and healthcare recommendation scenarios.