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Blog Post number 1
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honors
portfolio
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publications
CE‐DIFF: An Approach to Identifying and Coping with Irregular Ratings in Collaborative Decision Making
Published in Decision Sciences, 2021
Li Yu, Dongsong Zhang, Zhe Fu
TRACE: Travel Reinforcement Recommendation based on Location-aware Context Extraction
Published in ACM Transactions on Knowledge Discovery from Data, 2022
Zhe Fu, Li Yu, Xi Niu
Wisdom of Crowds and Fine-grained Learning for Serendipity Recommendations
Published in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
Zhe Fu, Xi Niu, Li Yu
How Does User Engagement Support Content Moderation? A Deep Learning-based Comparative Study
Published in Americas Conference on Information Systems Proceedings, 2023
Kanlun Wang, Zhe Fu, Lina Zhou, Dongsong Zhang
Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives
Published in ACM Computing Surveys, 2023
Zhe Fu, Xi Niu, Mary Lou Maher
Modeling Users’ Curiosity in Recommender Systems
Published in ACM Transactions on Knowledge Discovery from Data, 2023
Zhe Fu, Xi Niu
Leveraging Uncertainty Quantification for Reducing Data for Recommender Systems
Published in 2023 IEEE International Conference on Big Data, 2023
Xi Niu, Ruhani Rahman, Xiangcheng Wu, Riyi Qiu, Zhe Fu, Depeng Xu
Predicting Sales Lift of Influencer-generated Short Video Advertisements: A Ladder Attention-based Multimodal Time Series Forecasting Framework
Published in Proceedings of the 57th Hawaii International Conference on System Sciences, 2024
Zhe Fu, Kanlun Wang, Jianfei Wang, Yunqin Zhu
Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach
Published in Pacific-Asia Conference on Information Systems (PACIS) Proceedings, 2024
Zhe Fu, Kanlun Wang, Wangjiaxuan Xin, Lina Zhou, Shi Chen, Yaorong Ge, Daniel Janies, Dongsong Zhang (Best Complete Paper Award Winner of PACIS 2024)
The Art of Asking: Prompting Large Language Models for Serendipity Recommendations
Published in Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 2024
Zhe Fu, Xi Niu
From Interaction to Prediction: A Multi-interactive Attention-based Approach to Product Rating Prediction
Published in INFORMS Journal on Computing, 2025
Li Yu, Wei Gong, Dongsong Zhang, Yu Ding, Zhe Fu
MT-GPD: A Multimodal Deep Transfer Learning Model Enhanced by Auxiliary Mechanisms for Cross-Domain Online Fake News Detection
Published in Production and Operations Management, 2025
Dongsong Zhang, Guohou Shan, Minwoo Lee, Lina Zhou, Zhe Fu
A Deep Learning Model for Cross-domain Serendipity Recommendations
Published in ACM Transactions on Recommender Systems, 2025
Zhe Fu, Xi Niu, Xiangcheng Wu, Ruhani Rahman
Stylometric Characteristics of Code-Switched Offensive Language in Social Media
Published in Information & Management, 2025
Lina Zhou, Zhe Fu
Fine-tuning LLMs with Cross-Attention-based Weight Decay for Bias Mitigation
Published in Findings of the Conference on Empirical Methods in Natural Language Processing, 2025
Farsheed Haque, Zhe Fu, Depeng Xu, Shuhan Yuan, Xi Niu
Digital Voices of Survival: From Social Media Disclosures to Tailored Support for Domestic Violence Victims
Published in Proceedings of the 59th Hawaii International Conference on System Sciences, 2026
Kanlun Wang, Zhe Fu, Wangjiaxuan Xin, Lina Zhou, Shashi Kiran Chandrappa
teaching
Personalization and Recommender Systems
Postgraduate course, UNC Charlotte, Department of Software and Information Systems, 2023
An introduction to the application of personalization and recommender systems techniques in information systems. Topics include: historical, individual and commercial perspectives; underlying approaches to content-based and collaborative recommendation techniques for building user models; acceptance issues; and casestudies drawn from research prototypes and commercially deployed systems.
Data Mining
Postgraduate course, UNC Charlotte, Department of Software and Information Systems, 2025
This course is about data mining. It is an essential part of AI, which is one of the hottest topics in computer science today. Data mining is a fast-evolving field, especially for recent five years. The availability of large amounts of data has created unprecedented opportunities to leverage computational and statistical approaches to turn data into actionable knowledge. This course covers general techniques for analyzing large amounts of numeric and text data. The entire data mining process is covered in this course: setting up a problem, data preprocessing, model constructions, model evaluations, and interpretations in decision making. This course covers both classical data mining approach (e.g., Apriori, Random Forest, etc) as well as the recent deep learning models (e.g., RNN, CNN, BERT). In addition, the recent rise of large language models (LLMs), especially ChatGPT, has brought global excitement.