Reference Hub

This research has been cited in:

Conference
A Community-Driven Workflow Recommendations and Reuse Infrastructure2014 IEEE 8th International Symposium on Service Oriented System Engineering10.1109/SOSE.2014.23
Article
HyOASAM: A Hybrid Open API Selection Approach for Mashup DevelopmentMathematical Problems in Engineering10.1155/2020/4984375
Chapter
Digital Business Technology and RegulationDigital Business and Electronic Commerce10.1007/978-3-030-63482-7_5
Article
QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applicationsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.04.002
Chapter
Digital Government TechnologyDigital Government10.1007/978-3-031-13086-1_5
Article
Augmented latent Dirichlet allocation model via word embedded clusters for mashup service clusteringConcurrency and Computation: Practice and Experience10.1002/cpe.6896
Chapter
An API Recommendation Method Based on Beneficial InteractionCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-24383-7_4
Chapter
Research of Web Service Recommendation Using Bayesian Network ReasoningServices Computing – SCC 201810.1007/978-3-319-94376-3_2
Chapter
Effective Mashup Service Clustering Method by Exploiting LDA Topic Model from Multiple Data SourcesAdvances in Services Computing10.1007/978-3-319-26979-5_12
Chapter
E-Government TechnologieE-Government10.1007/978-3-662-65330-2_5
Article
Domain-aware Mashup service clustering based on LDA topic model from multiple data sourcesInformation and Software Technology10.1016/j.infsof.2017.05.001
Article
Deep learning-based open API recommendation for Mashup developmentScience China Information Sciences10.1007/s11432-021-3531-0
Article
Web Service Recommendation via Integrating Heterogeneous Graph Attention Network Representation and FiBiNET Score PredictionIEEE Transactions on Services Computing10.1109/TSC.2023.3287189
Article
End-to-End Web Service Recommendations by Extending Collaborative Topic RegressionInternational Journal of Web Services Research10.4018/IJWSR.2018010105
Chapter
An Approach of Role Updating in Context-Aware Role MiningSecuring the Internet of Things10.4018/978-1-5225-9866-4.ch066
Article
An Approach of Role Updating in Context-Aware Role MiningInternational Journal of Web Services Research10.4018/IJWSR.2017040102

Mashup Service Recommendation Based on Usage History and Service Network

Buqing Cao, Jianxun Liu, Mingdong Tang, Zibin Zheng, Guangrong Wang

Source Title: International Journal of Web Services Research (IJWSR)10(4)

ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781466635524|DOI: 10.4018/ijwsr.2013100104

Cite Article Cite Article

MLA

Cao, Buqing, et al. "Mashup Service Recommendation Based on Usage History and Service Network." IJWSR vol.10, no.4 2013: pp.82-101. http://doi.org/10.4018/ijwsr.2013100104

APA

Cao, B., Liu, J., Tang, M., Zheng, Z., & Wang, G. (2013). Mashup Service Recommendation Based on Usage History and Service Network. International Journal of Web Services Research (IJWSR), 10(4), 82-101. http://doi.org/10.4018/ijwsr.2013100104

Chicago

Cao, Buqing, et al. "Mashup Service Recommendation Based on Usage History and Service Network," International Journal of Web Services Research (IJWSR) 10, no.4: 82-101. http://doi.org/10.4018/ijwsr.2013100104

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

With the rapid development of Web2.0 and its related technologies, Mashup services (i.e., Web applications created by combining two or more Web APIs) are becoming a hot research topic. The explosion of Mashup services, especially the functionally similar or equivalent services, however, make services discovery more difficult than ever. In this paper, we present an approach to recommend Mashup services to users based on usage history and service network. This approach firstly extracts users' interests from their Mashup service usage history and builds a service network based on social relationships information among Mashup services, Web application programming interfaces (APIs) and their tags. The approach then leverages the target user's interest and the service social relationship to perform Mashup service recommendation. Large-scale experiments based on a real-world Mashup service dataset show that the authors' proposed approach can effectively recommend Mashup services to users with excellent performance. Moreover, a Mashup service recommendation prototype system is developed.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Mashup Service Recommendation Based on Usage History and Service Network

MLA

APA

Chicago

Export Reference

Abstract

Request Access