Novel Clustering-Based Web Service Recommendation Framework

The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, availability, etc. However, the limitation of these methods is that they are producing similar web services in recommendation lists. To address this research problem, the improved clustering-based web service recommendation method is proposed in this project. This approach is mainly to produce diversity in the results of web service recommendation. In this method, functional interest, QoS preference, and diversity features are combined to produce the unique recommendation list of web services to end-users. To produce the unique recommendation results, the researchers proposed a web service classify order that is clustering based on web service functional relevance such as non-useful pertinence, recorded client intrigue importance, potential client intrigue significance, etc.

Automated summary

This project proposes an improved clustering-based method for recommending web services, aiming to generate diverse recommendation results. The method combines functional interest, QoS preference, and diversity features to produce a unique recommendation list of web services.