Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Making medication prescriptions in response to the patient's diagnosis is a challenging task.
The number of pharmaceutical companies, their inventory of medicines, and the …

Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …

Recent advances in Large Language Models (LLMs) have been changing the paradigm of
Recommender Systems (RS). However, when items in the recommendation scenarios …

We propose DISC-MedLLM, a comprehensive solution that leverages Large Language
Models (LLMs) to provide accurate and truthful medical response in end-to-end …

Motivation In recent years, interest has arisen in using machine learning to improve the
efficiency of automatic medical consultation and enhance patient experience. In this article …

With the rapid evolution of transformer architectures, researchers are exploring their
application in sequential recommender systems (SRSs) and presenting promising …

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of
recommender systems. Due to its rich semantic content and associations among interactive …

In the realm of deep learning-based recommendation systems, the increasing computational
demands, driven by the growing number of users and items, pose a significant challenge to …

Medicine recommendation systems target to recommend a set of medicines given a set of
symptoms which play a crucial role in assisting doctors in their daily clinics. Existing …