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Personalized QoS Prediction forWeb Services via Collaborative Filtering
Abstract
Many researchers propose that, not only functional but also non-functional properties, also known as quality of service (QoS), should be taken into consideration when consumers select services. Consumers need to make prediction on quality of unused web services before selecting. Usually, this prediction is based on other consumers' experiences. Being aware of different QoS experiences of consumers, this paper proposes a collaborative filtering based approach to making similarity mining and prediction from consumers' experiences. Experimental results demonstrate that this approach can make significant improvement on the effectiveness of QoS prediction for web services.
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Citations
Cited by
WSRec: A Collaborative Filtering Based Web Service Recommender System
Zheng, Z., Ma, H., Lyu, M. R., & King, I. (2009) · 2009 IEEE International Conference on Web Services · doi:10.1109/icws.2009.30 · Cited by 448
Privacy Preserving Location-Aware Personalized Web Service Recommendations
Badsha, S., Yi, X., Khalil, I., Liu, D., Nepal, S., Bertino, E., & Lam, K. Y. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2839587 · Cited by 56
Metadata
- Authors
- Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., & Mei, H.
- Venue
- IEEE International Conference on Web Services (ICWS 2007)
- Publisher
- IEEE
- Published
- 2007-07
- Pages
- 439-446
- OpenAlex
- W2140597141
- Cited by
- 463
- References
- 0
- FWCI
- 8.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., & Mei, H. (2007). Personalized QoS Prediction forWeb Services via Collaborative Filtering. IEEE International Conference on Web Services (ICWS 2007). https://doi.org/10.1109/icws.2007.140
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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).
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