This website hosts supplementary material associated with manuscript DMKD-00855, submitted for consideration for publication in WIREs Data Mining and Knowledge Discovery.

Summary:

1. Results Derived from PlumX Metrics
2. Top-15 publications from 2023–2024 ranked according to their Field-Weighted Citation Impact (FWCI)
3. References Considered for Each Research Question

Results Derived from PlumX Metrics

PlumX is a research metrics tool developed by Elsevier that analyzes and visualizes the academic and societal impact of scholarly works (e.g., articles, books) by collecting data from multiple sources and categorizing them into five principal areas. Figure 1S and Table 1S present the following dimensions as annual averages: Usage (clicks, downloads, views, library holdings, video plays), Mentions (blog posts, comments, reviews, Wikipedia references, news media), and Captures/Readers (bookmarks, code forks, favorites, readers, watchers). Its purpose is to provide a comprehensive assessment of research impact that extends beyond traditional bibliographic citation metrics.

Top-15 publications from 2023–2024 ranked according to their Field-Weighted Citation Impact (FWCI)

• Afsar, M. M., Crump, T., and Far, B. (2022). Reinforcement Learning based Recommender Systems: A Survey. ACM Computing Surveys, 55(7) 1–38.
• Bonicalzi, S., De Caro, M., and Giovanola, B. (2023). Artificial Intelligence and Autonomy: On the Ethical Dimension of Recommender Systems. Topoi, 42(3) 819–832.
• Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., Zheng, K., Lian, D., and Chen, E. (2024). When large language models meet personalization: perspectives of challenges and opportunities. World Wide Web, 27(4).
• Chen, X., Yao, L., McAuley, J., Zhou, G., and Wang, X. (2023). Deep reinforcement learning in recommender systems: A survey and new perspectives. Knowledge‑Based Systems, 264 110335.
• da Silva, F. L., Slodkowski, B. K., da Silva, K. K. A., and Cazella, S. C. (2022). A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities. Education and Information Technologies, 28(3) 3289–3328.
• Deldjoo, Y., Nazary, F., Ramisa, A., McAuley, J., Pellegrini, G., Bellogin, A., and Noia, T. D. (2023). A Review of Modern Fashion Recommender Systems. ACM Computing Surveys, 56(4) 1–37.
• G. M., D., Goudar, R. H., Kulkarni, A. A., Rathod, V. N., and Hukkeri, G. S. (2024). A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review. IEEE Access, 12 34019–34041.
• Li, Y., Liu, K., Satapathy, R., Wang, S., and Cambria, E. (2024). Recent Developments in Recommender Systems: A Survey [Review Article]. IEEE Computational Intelligence Magazine, 19(2) 78–95.
• Valentine, L., D’Alfonso, S., and Lederman, R. (2022). Recommender systems for mental health apps: advantages and ethical challenges. AI & SOCIETY, 38(4) 1627–1638.
• Wang, S., Cao, L., Wang, Y., Sheng, Q. Z., Orgun, M. A., and Lian, D. (2021). A Survey on Session‑based Recommender Systems. ACM Computing Surveys, 54(7) 1–38.
• Wang, S., Wang, Y., Sivrikaya, F., Albayrak, S., and Anelli, V. W. (2023). Data science for next‑generation recommender systems. International Journal of Data Science and Analytics, 16(2) 135–145.
• Wang, Q., Wei, Y., Yin, J., Wu, J., Song, X., and Nie, L. (2023). DualGNN: Dual Graph Neural Network for Multimedia Recommendation. IEEE Transactions on Multimedia, 25 1074–1084.
• Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B. (2022). Graph Neural Networks in Recommender Systems: A Survey. ACM Computing Surveys, 55(5) 1–37.
• Zangerle, E. and Bauer, C. (2022). Evaluating Recommender Systems: Survey and Framework. ACM Computing Surveys, 55(8) 1–38.
• Zhang, M., Wu, S., Yu, X., Liu, Q., and Wang, L. (2022). Dynamic Graph Neural Networks for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering 1–1.

References Considered for Each Research Question

The search covered the period from 2012 to 2024 and was restricted to English-language research articles indexed in the Journal Citation Reports (JCR) and books. So, the inclusion criteria were:

1. Language: Studies must be published in English.
2. Publication Type: Only peer-reviewed research articles and books were considered.
3. Publication Period: Articles published between January 2012 and December 2024 were included.
4. Scope: The study must explicitly focus on RSs.
5. Methodological Clarity: The study must clearly describe the recommender system employed—whether existing or novel—and provide sufficient details regarding its implementation.
6. Outcomes: The study must report clear and well-defined outcomes resulting from the application of RSs within a specific research context.

These criteria ensured the inclusion of high-quality primary sources that comprehensively addressed both the theoretical foundations, practical developments and future directions of RSs. The complete list of selected references can be downloaded here.