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A Social-Aware Service Recommendation Approach for Mashup Creation

A Social-Aware Service Recommendation Approach for Mashup Creation

Jian Cao, Wenxing Xu, Liang Hu, Jie Wang, Minglu Li
Copyright: © 2013 |Volume: 10 |Issue: 1 |Pages: 20
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781466631373|DOI: 10.4018/jwsr.2013010103
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MLA

Cao, Jian, et al. "A Social-Aware Service Recommendation Approach for Mashup Creation." IJWSR vol.10, no.1 2013: pp.53-72. http://doi.org/10.4018/jwsr.2013010103

APA

Cao, J., Xu, W., Hu, L., Wang, J., & Li, M. (2013). A Social-Aware Service Recommendation Approach for Mashup Creation. International Journal of Web Services Research (IJWSR), 10(1), 53-72. http://doi.org/10.4018/jwsr.2013010103

Chicago

Cao, Jian, et al. "A Social-Aware Service Recommendation Approach for Mashup Creation," International Journal of Web Services Research (IJWSR) 10, no.1: 53-72. http://doi.org/10.4018/jwsr.2013010103

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Abstract

Mashup is a user-centric approach to create value-added new services by utilizing and recombining existing service components. However, as services become increasingly more spontaneous and prevalent on the Internet, finding suitable services from which to develop a mashup based on users’ explicit and implicit requirements remains a daunting task. Several approaches already exist for recommending specific services for users but they are limited to proposing only services with similar functionality. In order to recommend a set of suitable services for a general mashup based on users’ functional specifications, a novel social-aware service recommendation approach, where multi-dimensional social relationships among potential users, topics, mashups, and services are described by a coupled matrices model, is proposed in this paper. Accordingly, a factorization algorithm is designed to predict unobserved relationships, and we use a genetic algorithm to learn some specific parameters, and then construct a comprehensive service recommendation model. Experimental results for a realistic mashup data set indicate that the proposed approach outperforms other state-of-the-art methods.

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