World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
12417
World Ranking
4080
National Ranking
1937

Overview

Yongfeng Zhang is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily falls within the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, as well as Management Science and Operations Research.

The main topics addressed in their work cover Recommender Systems and Techniques, Topic Modeling, Advanced Graph Neural Networks, Explainable Artificial Intelligence (XAI), Natural Language Processing Techniques, Advanced Bandit Algorithms Research, and Multimodal Machine Learning Applications.

Their recent publication record showcases contributions to various academic venues. Selected recent papers include:

  • CLIP-Adapter: Better Vision-Language Models with Feature Adapters, 2023, International Journal of Computer Vision
  • Efficient Neural Matrix Factorization without Sampling for Recommendation, 2020, ACM Transactions on Information Systems
  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Personalized Prompt Learning for Explainable Recommendation, 2023, ACM Transactions on Information Systems
  • CLIP-Adapter: Better Vision-Language Models with Feature Adapters, 2021, arXiv (Cornell University)

Yongfeng Zhang frequently publishes in several venues, including:

  • arXiv (Cornell University) with 69 publications
  • ACM Transactions on Information Systems with 6 publications
  • ACM Transactions on Recommender Systems with 6 publications
  • ACM Transactions on Intelligent Systems and Technology with 4 publications
  • Proceedings of the ACM Web Conference 2022 with 3 publications

Their collaborative network involves frequent co-authors such as Shuyuan Xu, Wenyue Hua, Juntao Tan, Yingqiang Ge, and Zuohui Fu, reflecting ongoing partnerships in research projects.

Best Publications

  • Explainable Recommendation: A Survey and New Perspectives

    Yongfeng Zhang;Xu Chen

  • Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

    Yongfeng Zhang;Guokun Lai;Min Zhang;Yi Zhang

  • Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

    Yikun Xian;Zuohui Fu;S. Muthukrishnan;Gerard de Melo

  • Sequential Recommendation with User Memory Networks

    Xu Chen;Hongteng Xu;Yongfeng Zhang;Jiaxi Tang

  • Learning heterogeneous knowledge base embeddings for explainable recommendation

    Qingyao Ai;Vahid Azizi;Xu Chen;Yongfeng Zhang

  • Towards Conversational Search and Recommendation: System Ask, User Respond

    Yongfeng Zhang;Xu Chen;Qingyao Ai;Liu Yang

  • BERT with History Answer Embedding for Conversational Question Answering

    Chen Qu;Liu Yang;Minghui Qiu;W. Bruce Croft

  • Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

    Yongfeng Zhang;Qingyao Ai;Xu Chen;W. Bruce Croft

  • Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation

    Xu Chen;Hanxiong Chen;Hongteng Xu;Yongfeng Zhang

  • Understanding Echo Chambers in E-commerce Recommender Systems

    Yingqiang Ge;Shuya Zhao;Honglu Zhou;Changhua Pei

  • Counterfactual Explainable Recommendation

    Juntao Tan;Shuyuan Xu;Yingqiang Ge;Yunqi Li

  • Fairness-Aware Explainable Recommendation over Knowledge Graphs

    Zuohui Fu;Yikun Xian;Ruoyuan Gao;Jieyu Zhao

  • Detecting Stress Based on Social Interactions in Social Networks

    Huijie Lin;Jia Jia;Jiezhong Qiu;Yongfeng Zhang

  • User-oriented Fairness in Recommendation

    Yunqi Li;Hanxiong Chen;Zuohui Fu;Yingqiang Ge

  • Efficient Neural Matrix Factorization without Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Yiqun Liu

  • Personalized re-ranking for recommendation

    Changhua Pei;Yi Zhang;Yongfeng Zhang;Fei Sun

  • Towards Long-term Fairness in Recommendation

    Yingqiang Ge;Shuchang Liu;Ruoyuan Gao;Yikun Xian

  • Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation

    Xin Xin;Xiangnan He;Yongfeng Zhang;Yongdong Zhang

  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Weizhi Ma

  • Learning to Rank Features for Recommendation over Multiple Categories

    Xu Chen;Zheng Qin;Yongfeng Zhang;Tao Xu

  • Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

    Liu Yang;Minghui Qiu;Chen Qu;Jiafeng Guo

  • Dynamic Explainable Recommendation Based on Neural Attentive Models

    Xu Chen;Yongfeng Zhang;Zheng Qin

  • Counterfactual Data-Augmented Sequential Recommendation

    Zhenlei Wang;Jingsen Zhang;Hongteng Xu;Xu Chen

Frequent Co-Authors

Min Zhang
Min Zhang Tsinghua University
W. Bruce Croft
W. Bruce Croft University of Massachusetts Amherst
Shaoping Ma
Shaoping Ma Tsinghua University
Fei Sun
Fei Sun Institute Of Computing Technology
Gerard de Melo
Gerard de Melo Hasso Plattner Institute
Li Chen
Li Chen Hong Kong Baptist University
Jiafeng Guo
Jiafeng Guo Chinese Academy of Sciences
Qi Zhao
Qi Zhao University of Minnesota
Xiangnan He
Xiangnan He University of Science and Technology of China
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen

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