Name
Abstract | ||
|---|---|---|
With the development of Service-Oriented technologies, the amount of Web services grows rapidly. QoS-Aware Web service recommendation can help service users to design more efficient service-oriented systems. However, existing methods assume the QoS information for service users are all known and accurate, but in real case, there are always many missing QoS values in history records, which increase the difficulty of the missing QoS value prediction. By considering the user-service-time three dimension context information, we study a Temporal QoS-Aware Web Service Recommendation Framework which aims to recommend best candidates to service user's requirements and meanwhile improve the QoS prediction accuracy. We propose to envision such QoS value data as a tensor which is known as multi-way array, and formalize this problem as a tensor factorization model and propose a Tucker decomposition (TD)algorithm which is able to deal with the triadic relations of user-service-time model. However, one major challenge is that how to deal with the dynamic incoming service QoS value data streams. Thus, we introduce the incremental tensor factorization (ITF)method which is (a) scalable, (b) space efficient (it does not need to store the past data). Extensive experiments are conducted based on our real-world QoS dataset collected on Planet-Lab, comprised of service invocation response-time values from 408 users on 5,473 Web services at 240 time periods. Comprehensive empirical studies demonstrate that our approach is faster and more accuracy than other approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICDMW.2014.176 | ICDM Workshops |
Keywords | Field | DocType |
incremental tensor factorization approach,service-oriented technologies,service invocation response-time values,web services,user-service-time three dimension context information,quality of service,incremental tensor factorization method,history records,qos-aware web service recommendation,triadic relations,td algorithm,tucker decomposition algorithm,itf method,temporal qos,qos prediction accuracy,multiway array,tensors,tensile stress,prediction algorithms,matrix decomposition | Data mining,Data stream mining,Tensor,Computer science,Matrix decomposition,Quality of service,Tucker decomposition,Artificial intelligence,Web service,Machine learning,Empirical research,Scalability | Conference |
Citations | PageRank | References |
3 | 0.37 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wancai Zhang | 1 | 33 | 2.14 |
| Hailong Sun | 2 | 680 | 64.83 |
| Xudong Liu | 3 | 769 | 100.74 |
| Xiaohui Guo | 4 | 38 | 4.90 |
| guo | 5 | 11 | 1.46 |