Comparative Analysis of Methodologies and Approaches in Recommender Systems Utilizing Large Language Models

Information Science, Computer Science, Artificial Intelligence,
Recommender Systems   Large Language Models   Natural Language Processing   User Modeling   Collaborative Filtering   Parameter-Efficient Fine-Tuning  

Academic Background With the explosive growth of internet information, recommender systems (RSs) have become indispensable in modern digital life. Whether it’s movie recommendations on Netflix or personalized news feeds on social media, recommender systems are reshaping users’ online experiences. However, traditional recommender systems face numero...

A Mutual Supervision Framework for Referring Expression Segmentation and Generation

A Mutual Supervision Framework for Referring Expression Segmentation and Generation

Information Science, Computer Science, Artificial Intelligence,
Referring Expression Segmentation   Referring Expression Generation   Mutual Supervision   Transformer   Computer Vision   Natural Language Processing   Image Segmentation  

A Mutual Supervision Framework for Referring Expression Segmentation and Generation Research Background and Problem Statement In recent years, vision-language interaction technology has made remarkable progress in the field of artificial intelligence. Among these advancements, referring expression segmentation (RES) and referring expression generat...

Using Large Language Models to Assess Public Perceptions Around Glucagon-Like Peptide-1 Receptor Agonists on Social Media

Information Science, Computer Science, Artificial Intelligence, Medicine, Environmental and Public Health, Endocrinology, Cardiovascular System,
Obesity   Glucagon-like Peptide-1 Receptor Agonists   Social Media   Public Perception   Natural Language Processing  

In the global context, the prevalence of obesity is on the rise, bringing significant impacts to public health. Obesity is independently associated with the incidence and mortality of cardiovascular diseases, with an estimated economic burden exceeding $200 billion annually for healthcare systems. In recent years, glucagon-like peptide-1 (GLP-1) re...

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records

Information Science, Computer Science, Artificial Intelligence,
Electronic Health Records   Heterogeneous Graph Convolutional Networks   Text Classification   Natural Language Processing   Deep Learning  

EHR-HGCN: A Novel Hybrid Heterogeneous Graph Convolutional Network Method for Electronic Health Record Text Classification Academic Background With the rapid development of Natural Language Processing (NLP), text classification has become an important research direction in this field. Text classification not only helps us understand the knowledge b...