Collaborative Web Service Quality Prediction via Exploiting Matrix Factorization and Network Map

• Published in: IEEE eTransactions on network and service management Vol. 13; no. 1; pp. 126 - 137
• Main Authors: Tang, Mingdong, Zheng, Zibin, Kang, Guosheng, Liu, Jianxun, Yang, Yatao, Zhang, Tingting
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collaboration; collaborative filtering; Factorization; Filtering; Filtration; Historic; Internet service providers; Mathematical models; matrix factorization; Networks; prediction; QoS; Quality of service; Quality of service architectures; Service introduction; Sparse matrices; Throughput; Time factors; Web services; prediction; collaborative filtering; matrix factorization; Web services; QoS
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2016.2517097

Quality of services (QoS) is an important concern in Web service recommendation or selection. Predicting QoS values of Web services based on their historical QoS records is an effective way to acquire Web service QoS, and thus has attracted considerable research interests. Recently, matrix factorization (MF), a well-known model-based collaborative filtering (CF) technique, has been successfully applied to the Web service QoS prediction. It is generally believed that MF can significantly outperform traditional memory-based CF techniques. However, previous work seldom considered the influence of the underlying network on Web service QoS when adopting MF for Web service QoS prediction. Hence, the prediction performance is not good enough. In this paper, we propose a network-aware Web service QoS prediction approach by integrating MF with the network map. By employing the network map, network distances between service users can be measured and neighborhoods of users are identified. Then, the traditional MF model is revamped by incorporating the constraint term that neighbor users are likely to perceive similar QoS of Web services. Experiments conducted on two real-world Web service datasets indicate that our approach outperforms previous MF and CF-based approaches in prediction accuracy.