The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
Open accessAbstract
Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.
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Cites
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Cited by
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Ko, H., Lee, S., Park, Y., & Choi, A. (2022) · Electronics · doi:10.3390/electronics11010141 · Cited by 658
Theodorakopoulos, L., & Theodoropoulou, A. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/3641502 · Cited by 153
Beyari, H., & Hashem, T. (2025) · Behavioral Sciences · doi:10.3390/bs15050700 · Cited by 53
Metadata
- Authors
- Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-11-02
- Volume
- 10
- Issue
- 21
- Pages
- 7748
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3097103183
- Cited by
- 427
- References
- 1
- FWCI
- 39.28
- Type
- journal article
- Language
- en
- Open access
- gold
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- Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Applied Sciences. https://doi.org/10.3390/app10217748
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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.
