Name
Abstract | ||
|---|---|---|
It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users' interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user's score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2847631 | IEEE ACCESS |
Keywords | Field | DocType |
Association rules,clustering,heterogeneous information network,personalized cloud service recommendation,user interest model | Convergence (routing),Data mining,Similarity measure,Computer science,Local optimum,Peer to peer computing,Association rule learning,Cluster analysis,Cloud computing,Personalization,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengwen Zhang | 1 | 141 | 12.15 |
| Zengcheng Li | 2 | 0 | 0.34 |
| Tang Li | 3 | 0 | 0.34 |
| Yunan Han | 4 | 0 | 0.34 |
| Cuicui Wei | 5 | 0 | 0.34 |
| Yongqiang Cheng | 6 | 133 | 29.99 |
| Yonghong Peng | 7 | 400 | 33.39 |