Name
Abstract | ||
|---|---|---|
Quality of Services (QoS) is an important criterion to evaluate Web services recommendation system. Due to factors including various network conditions, QoS values are dynamic and time-varying. In reality, the data is too spare to fit in with traditional time series forecasting model (e.g., ARIMA). To address this crucial challenge, this paper proposes a novel time-aware and sparsity-tolerant QoS values prediction approach based on collaborative filtering. Our approach combines limited historical QoS value with collaborative filtering method to forecast the personalized QoS values. Based on the limited data, our approach firstly forecasts user-service pairs that have historical usage experiences, and then uses CF-based method to predict personalized QoS values. Finally, we combine the results from temporal forecasting with those from CF prediction as the final forecasted QoS values. The extensive experiments show that the proposed approach efficiently improves the forecasting coverage and accuracy. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICWS.2016.88 | 2016 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
QoS prediction,time-aware,K-means clustering,collaborative filtering,Web services | Recommender system,Data mining,Time series,k-means clustering,Collaborative filtering,Spare part,Computer science,Quality of service,Autoregressive integrated moving average,Web service | Conference |
ISBN | Citations | PageRank |
978-1-5090-2676-0 | 4 | 0.40 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Wu | 1 | 27 | 1.16 |
| Weiwei Qiu | 2 | 85 | 4.50 |
| xinyu | 3 | 590 | 30.19 |
| Zibin Zheng | 4 | 3731 | 199.37 |
| Xiaohu Yang | 5 | 125 | 8.71 |