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Recent Developments in Recommender Systems: A Survey

delete2024-05-01
delete7
PRE
AI
Y
Yang Li *
K
Kangbo Liu
R
Ranjan Satapathy
王苏杭 (Suhang Wang)
E
Erik Cambria
DOI:10.1109/MCI.2024.3363984delete
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Abstract

Abstract

En 中文
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.
Keywords:
Collaborative software
Recommender systems
Surveys
Taxonomy
Group recommendation
personalized recommendation
recommender system

Journal

IEEE Computational Intelligence Magazine cover
IEEE Computational Intelligence Magazine
IF:
11.2
Papers:
606
Citations:
3.1K

Organization

A
Arizona State University
Scholars:
2.7W
Papers: 2.5W
Citations: 4.2W
N
Northwestern Polytechnical University
Scholars:
4.6W
Papers: 3.7W
Citations: 5.3W
A
agency for science technology & research (a*star)
Scholars:
2.2W
Papers: 1.9W
Citations: 57
A
arizona state university-tempe
Scholars:
1.5W
Papers: 1.2W
Citations: 13
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