World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
9723
World Ranking
6162
National Ranking
814

Overview

Li Chen is affiliated with Hong Kong Baptist University in China and has contributed extensively to the fields of Computer Science and Social Sciences. Their research primarily covers areas such as Artificial Intelligence, Sociology and Political Science, Information Systems, Computer Vision and Pattern Recognition, and Marketing.

The main topics explored in Li Chen's work include:

  • Recommender Systems and Techniques
  • Topic Modeling
  • Digital Marketing and Social Media
  • Privacy, Security, and Data Protection
  • Natural Language Processing Techniques
  • Advanced Text Analysis Techniques
  • Consumer Behavior in Brand Consumption and Identification

Li Chen's publication record features papers in several prominent venues. Frequently publishing in venues such as arXiv (Cornell University), SSRN Electronic Journal, ACM Transactions on Recommender Systems, User Modeling and User-Adapted Interaction, and Frontiers in Psychology, their research output spans both interdisciplinary and specialized knowledge domains.

Recent notable papers authored or co-authored by Li Chen include:

  • What drives digital engagement with sponsored videos? An investigation of video influencers' authenticity management strategies, 2022, Journal of the Academy of Marketing Science
  • Personalized Prompt Learning for Explainable Recommendation, 2023, ACM Transactions on Information Systems
  • Explainable Prediction of Medical Codes With Knowledge Graphs, 2020, Frontiers in Bioengineering and Biotechnology
  • Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems, 2022, CHI Conference on Human Factors in Computing Systems
  • A grey seasonal least square support vector regression model for time series forecasting, 2020, ISA Transactions

Collaboration plays a significant role in Li Chen's research activities. Frequent co-authors include Yongfeng Zhang, Yucheng Jin, Wanling Cai, Yuhan Zhao, and Lei Li, with counts of co-authorship ranging from six to eight publications each. This network reflects engagement with diverse experts across related research fields.

Best Publications

  • A user-centric evaluation framework for recommender systems

    Pearl Pu;Li Chen;Rong Hu

  • News impact on stock price return via sentiment analysis

    Xiaodong Li;Haoran Xie;Li Chen;Jianping Wang

  • Temporal recommendation on graphs via long- and short-term preference fusion

    Liang Xiang;Quan Yuan;Shiwan Zhao;Li Chen

  • Evaluating recommender systems from the user's perspective: survey of the state of the art

    Pearl Pu;Li Chen;Rong Hu

  • Recommender systems based on user reviews: the state of the art

    Li Chen;Guanliang Chen;Feng Wang

  • A Survey on Conversational Recommender Systems

    Dietmar Jannach;Ahtsham Manzoor;Wanling Cai;Li Chen

  • Trust building with explanation interfaces

    Pearl Pu;Li Chen

  • Critiquing-based recommenders: survey and emerging trends

    Li Chen;Pearl Pu

  • Trust-inspiring explanation interfaces for recommender systems

    Pearl Pu;Li Chen

  • Survey of Preference Elicitation Methods

    Li Chen;Pearl Pu

  • GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering

    Weike Pan;Li Chen

  • Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation

    Quan Yuan;Li Chen;Shiwan Zhao

  • Personalized Prompt Learning for Explainable Recommendation

    Unknown

  • Comparison of feature-level learning methods for mining online consumer reviews

    Li Chen;Luole Qi;Feng Wang

  • Human Decision Making and Recommender Systems

    Li Chen;Marco de Gemmis;Alexander Felfernig;Pasquale Lops

  • Personality and Recommender Systems

    Marko Tkalcic;Li Chen

  • Generate Neural Template Explanations for Recommendation

    Lei Li;Yongfeng Zhang;Li Chen

  • How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation

    Li Chen;Yonghua Yang;Ningxia Wang;Keping Yang

  • User-Involved Preference Elicitation for Product Search and Recommender Systems

    Pearl Pu;Li Chen

  • Personalized Transformer for Explainable Recommendation

    Lei Li;Yongfeng Zhang;Li Chen

  • Prompt Distillation for Efficient LLM-based Recommendation

    Unknown

  • Generating virtual ratings from chinese reviews to augment online recommendations

    Weishi Zhang;Guiguang Ding;Li Chen;Chunping Li

  • Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy

    Haoran Xie;Xiaodong Li;Tao Wang;Raymond Y.K. Lau

  • Human decision making and recommender systems

    Anthony Jameson;MC Martijn Willemsen;Alexander Felfernig;Marco de Gemmis

Frequent Co-Authors

Pearl Pu
Pearl Pu École Polytechnique Fédérale de Lausanne
Alexander Felfernig
Alexander Felfernig Graz University of Technology
Michelle X. Zhou
Michelle X. Zhou IBM (United States)
Haoran Xie
Haoran Xie Lingnan University
Yongfeng Zhang
Yongfeng Zhang Rutgers, The State University of New Jersey
Jiming Liu
Jiming Liu Hong Kong Baptist University
Pasquale Lops
Pasquale Lops University of Bari Aldo Moro
Giovanni Semeraro
Giovanni Semeraro University of Bari Aldo Moro
Francesco Ricci
Francesco Ricci Free University of Bozen-Bolzano
Marco de Gemmis
Marco de Gemmis University of Bari Aldo Moro

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