227 citations · 768 across the 50 of their papers we have counts for
50 papers
Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives
Wei Zeng, Hengshu Zhu, Chuan Qin +8
The ongoing evolution of AI paradigms has propelled AI research into the agentic AI stage. Consequently, the focus of research has shifted from single agents and simple application…
Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach
Yang Ji, Ying Sun, Hengshu Zhu
In the era of the knowledge economy, understanding how job skills influence salary is crucial for promoting recruitment with competitive salary systems and aligned salary expectati…
SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models
Chuan Qin, Xin Chen, Chengrui Wang +13
In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discov…
A Comprehensive Survey on Self-Interpretable Neural Networks
Yang Ji, Ying Sun, Yuting Zhang +7
Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making s…
Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective
Meng Xiao, Weiliang Zhang, Xiaohan Huang +4
Gene panel selection aims to identify the most informative genomic biomarkers in label-free genomic datasets. Traditional approaches, which rely on domain expertise, embedded machi…
Labor Migration Modeling through Large-scale Job Query Data
Zhuoning Guo, Le Zhang, Hengshu Zhu +3
Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, exis…
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting
Zhuoning Guo, Hao Liu, Le Zhang +3
Labor market forecasting on talent demand and supply is essential for business management and economic development. With accurate and timely forecasts, employers can adapt their re…
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models
Feihu Jiang, Chuan Qin, Kaichun Yao +4
Efficient knowledge management plays a pivotal role in augmenting both the operational efficiency and the innovative capacity of businesses and organizations. By indexing knowledge…
JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Zhihao Guan, Jia-Qi Yang, Yang Yang +3
Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent…
Harnessing Large Language Models for Text-Rich Sequential Recommendation
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu +2
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textua…
Make Large Language Model a Better Ranker
Wen-Shuo Chao, Zhi Zheng, Hengshu Zhu +1
Large Language Models (LLMs) demonstrate robust capabilities across various fields, leading to a paradigm shift in LLM-enhanced Recommender System (RS). Research to date focuses on…
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
Wenshuo Chao, Zhaopeng Qiu, Likang Wu +4
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…
Large Language Models are Not Stable Recommender Systems
Tianhui Ma, Yuan Cheng, Hengshu Zhu +1
With the significant successes of large language models (LLMs) in many natural language processing tasks, there is growing interest among researchers in exploring LLMs for novel re…
Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach
Haiping Ma, Changqian Wang, Hengshu Zhu +3
Cognitive diagnosis is a crucial task in computational education, aimed at evaluating students' proficiency levels across various knowledge concepts through exercises. Current mode…
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment
Chuang Zhao, Hongke Zhao, Hengshu Zhu +4
Unsupervised domain adaptation aims to transfer rich knowledge from the annotated source domain to the unlabeled target domain with the same label space. One prevalent solution is…
Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution
Ying Sun, Hengshu Zhu, Hui Xiong
Self-interpreting neural networks have garnered significant interest in research. Existing works in this domain often (1) lack a solid theoretical foundation ensuring genuine inter…
Enhancing Job Recommendation through LLM-based Generative Adversarial Networks
Yingpeng Du, Di Luo, Rui Yan +4
Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability. While existing job r…
Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
Qi Liu, Zheng Gong, Zhenya Huang +5
Machine learning algorithms have become ubiquitous in a number of applications (e.g. image classification). However, due to the insufficient measurement of traditional metrics (e.g…
Generative Job Recommendations with Large Language Model
Zhi Zheng, Zhaopeng Qiu, Xiao Hu +3
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recomme…
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
Likang Wu, Zhaopeng Qiu, Zhi Zheng +2
Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for be…
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
Chuan Qin, Le Zhang, Yihang Cheng +8
In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Ind…
Reciprocal Sequential Recommendation
Bowen Zheng, Yupeng Hou, Wayne Xin Zhao +2
Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RR…
Preference or Intent? Double Disentangled Collaborative Filtering
Chao Wang, Hengshu Zhu, Dazhong Shen +2
People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both…
A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu +9
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 Recomm…