Name
Abstract | ||
|---|---|---|
With the overwhelming increase of web services on the Internet, how to accurately perform QoS prediction has played a key role in service recommendation. Recently, three kinds of approaches have been presented on service QoS prediction based on collaborative filtering (CF), including user-intensive, service-intensive and their combination. However, the deficiency of current approaches is that all of the services invoked by target user (or all of the users who invoked target service) are applied to calculate average QoS, without the reduction to those dissimilar with target service (or target user). In this paper, we propose a reinforced collaborative filtering approach, where both similar users and services are integrally considered into a singleton CF. The experiments are conducted on a large-scale dataset called WS-DREAM, involving 5,825 real-world Web services in 73 countries and 339 service users in 30 countries. The experimental results demonstrate that our approach for QoS prediction outperforms the competing approaches. |
Year | Venue | Field |
|---|---|---|
2018 | ICSOC | Data mining,Collaborative filtering,Qos aware,Computer science,Quality of service,Web service,Multimedia,Service-oriented architecture,The Internet |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
14 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guobing Zou | 1 | 95 | 20.12 |
| Ming Jiang | 2 | 1 | 1.02 |
| Sen Niu | 3 | 7 | 3.17 |
| Hao Wu | 4 | 271 | 46.88 |
| Shengye Pang | 5 | 1 | 1.03 |
| Yanglan Gan | 6 | 13 | 3.96 |