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Recent Developments in Recommender Systems: A Survey [Review Article]

  • Yang Li
  • , Kangbo Liu
  • , Ranjan Satapathy
  • , Suhang Wang
  • , Erik Cambria
  • Northwestern Polytechnical University Xian
  • Agency for Science, Technology and Research, Singapore
  • Arizona State University
  • Nanyang Technological University

科研成果: 期刊稿件文章同行评审

95 引用 (Scopus)

摘要

In this technical survey, the latest advancements in the field of recommender systems are comprehensively summarized. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. It starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, it provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field.

源语言英语
页(从-至)78-95
页数18
期刊IEEE Computational Intelligence Magazine
19
2
DOI
出版状态已出版 - 1 5月 2024

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