Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment
With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation base...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/3437854 |
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| _version_ | 1850171231621349376 |
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| author | Yanwei Xu Lianyong Qi Wanchun Dou Jiguo Yu |
| author_facet | Yanwei Xu Lianyong Qi Wanchun Dou Jiguo Yu |
| author_sort | Yanwei Xu |
| collection | DOAJ |
| description | With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation. |
| format | Article |
| id | doaj-art-9cb2313915094109a5bcf28cfce239a2 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9cb2313915094109a5bcf28cfce239a22025-08-20T02:20:19ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/34378543437854Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud EnvironmentYanwei Xu0Lianyong Qi1Wanchun Dou2Jiguo Yu3School of Information Science and Engineering, Chinese Academy of Education Big Data, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Chinese Academy of Education Big Data, Qufu Normal University, Rizhao 276826, ChinaState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, ChinaSchool of Information Science and Engineering, Chinese Academy of Education Big Data, Qufu Normal University, Rizhao 276826, ChinaWith the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.http://dx.doi.org/10.1155/2017/3437854 |
| spellingShingle | Yanwei Xu Lianyong Qi Wanchun Dou Jiguo Yu Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment Complexity |
| title | Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment |
| title_full | Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment |
| title_fullStr | Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment |
| title_full_unstemmed | Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment |
| title_short | Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment |
| title_sort | privacy preserving and scalable service recommendation based on simhash in a distributed cloud environment |
| url | http://dx.doi.org/10.1155/2017/3437854 |
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