Enhancing Healthcare Recommendation Systems with a Multimodal LLMs-based MOE Architecture

Understand

With the increasing availability of multimodal data, many fields urgently require advanced architectures capable of effectively integrating these diverse data sources to address specific problems.

• This study proposes a hybrid recommendation model that combines the Mixture of Experts (MOE) framework with large language models to enhance the performance of recommendation systems in the healthcare domain.
• We built a small dataset for recommending healthy food based on patient descriptions and evaluated the model's performance on several key metrics, including Precision, Recall, NDCG, and MAP@5.
• The experimental results show that the hybrid model outperforms the baseline models, which use MOE or large language models individually, in terms of both accuracy and personalized recommendation effectiveness.