Name
Abstract | ||
|---|---|---|
Nowadays, more and more service consumers pay great attention to QoS (Quality of Service) when they find and select appropriate Web services. For most of the approaches to QoS-aware Web service recommendation, the list of Web services recommended to target users is generally obtained based on rating-oriented predictions, aiming at predicting the potential ratings that a target user may assign to the unrated services as accurately as possible. However, in some scenarios, high accuracy of rating predictions may not necessarily lead to satisfactory recommendation results. In this paper, we propose a ranking-oriented hybrid approach by combining item-based collaborative filtering techniques and latent factor models to address the problem of Web services ranking. In particular, the similarity between two Web services is measured in terms of the correlation coefficient between their rankings instead of between their ratings. Comprehensive experiments on the QoS data set composed of real-world Web services are conducted to test our approach, and the experimental results demonstrate that our approach outperforms other competing approaches. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SCC.2015.84 | SCC |
Keywords | Field | DocType |
quality of service, Web service recommendation, rating, ranking | Correlation coefficient,Data mining,Data modeling,Collaborative filtering,Qos aware,Ranking,Computer science,Quality of service,Factor analysis,Web service | Conference |
Citations | PageRank | References |
5 | 0.45 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingming Chen | 1 | 8 | 0.85 |
| Yu-Tao Ma | 2 | 314 | 28.89 |
| Bo Hu | 3 | 22 | 4.70 |
| Liang-Jie Zhang | 4 | 982 | 138.17 |