The DOI matches its Crossref record; 148 of the 148 references it cites were found in a registry or an index.
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Open accessAbstract
This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.
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1 retracted
WITHDRAWN: Big data analytic of contexts and cascading tourism for smart city
Balasaraswathi, M., Srinivasan, K., Udayakumar, L., Sivasakthiselvan, S., & Sumithra, M. G. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.10.132 · Cited by 30
RetractedThe Concept of a Linguistic Variable and Its Application to Approximate Reasoning—I
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Neural Collaborative Filtering
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Cited by
Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
Huang, X., Lian, J., Lei, Y., Yao, J., Lian, D., & Xie, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3731446 · Cited by 89
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
Huang, X., Lian, J., Lei, Y., Yao, J., Lian, D., & Xie, X. (2023) · arXiv · doi:10.48550/arxiv.2308.16505 · Cited by 89
Human-inspired Perspectives: A Survey on AI Long-term Memory
He, Z., Lin, W., Zheng, H., Zhang, F., Jones, M. W., Aitchison, L., Xu, X., Liu, M., Kristensson, P. O., & Shen, J. (2024) · arXiv · doi:10.48550/arxiv.2411.00489 · Cited by 3
Metadata
- Authors
- Ko, H., Lee, S., Park, Y., & Choi, A.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2022-01-03
- Volume
- 11
- Issue
- 1
- Pages
- 141
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4205390421
- Cited by
- 658
- References
- 148
- FWCI
- 135.72
- Type
- journal article
- Language
- en
- Open access
- gold
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
