Recent Developments in Recommender Systems: A Survey

June 22, 2023 ยท The Cartographer ยท ๐Ÿ› IEEE Computational Intelligence Magazine

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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Authors Yang Li, Kangbo Liu, Ranjan Satapathy, Suhang Wang, Erik Cambria arXiv ID 2306.12680 Category cs.IR: Information Retrieval Cross-listed cs.AI, cs.LG Citations 76 Venue IEEE Computational Intelligence Magazine Last Checked 1 day ago
Abstract
In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. 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. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based 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, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field.
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