Search Results for author:
Found 11 papers, 5 papers with code
DeltaNet: Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
Harnessing Large Language Models for Text-Rich Sequential Recommendation
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS).
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market.
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application.
Generative Job Recommendations with Large Language Model
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.
A Survey on Large Language Models for Recommendation
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries
We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance.
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically.
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback
On one hand, the user may exit immediately after clicking the news as he dislikes the news content, leaving the noise in his positive implicit feedback; on the other hand, the user may be recommended multiple interesting news at the same time and only click one of them, producing the noise in his negative implicit feedback.
Conditional Generation Net for Medication Recommendation
Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics.
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests.
