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This article introduces a clustering-based semantic service selection model (SSSM) and a user preferential model (UPM) aimed at enhancing web service selection based on quality of service (QoS) requirements. The proposed models improve the efficiency of service selection by mapping functional and nonfunctional requirements to discovered services, utilizing a two-tier user preference system. Experimental results demonstrate improved precision, recall, and F-measure values, indicating the effectiveness of the

Author

sana

Language

EN