Abstract
Quality of service (QoS) has been playing an increasingly important role in today's Web service environment. Many techniques have been proposed to recommend personalized Web services to customers. However, existing methods only utilize the QoS information at the client-side and neglect the contextual characteristics of the service. Based on the fact that the quality of Web service is affected by its context feature, this paper proposes a new QoS-aware Web service recommendation system, which considers the contextual feature similarities of different services. The proposed system first extracts the contextual properties from WSDL files to cluster Web services based on their feature similarities, and then utilizes an improved matrix factorization method to recommend services to users. The proposed framework is validated on a real-world dataset consisting of over 1.5 million Web service invocation records from 5825 Web services and 339 users. The experimental results prove the efficiency and accuracy of the proposed method. © 2017 IEEE.
| Original language | English |
|---|---|
| Article number | 7896643 |
| Pages (from-to) | 332-342 |
| Journal | IEEE Transactions on Network and Service Management |
| Volume | 14 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Jun 2017 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- matrix factorization
- QoS prediction
- recommendation system
- Web service
- Web service clustering