Collaborative personal profiling for web service ranking and recommendation

Wenge Rong, Baolin Peng, Yuanxin Ouyang (), Kecheng Liu and Zhang Xiong
Additional contact information
Wenge Rong: Beihang University
Baolin Peng: Beihang University
Yuanxin Ouyang: Beihang University
Kecheng Liu: University of Reading
Zhang Xiong: Beihang University

Information Systems Frontiers, 2015, vol. 17, issue 6, No 7, 1265-1282

Abstract: Abstract Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

Keywords: Web service; Discovery; Personalisation; Ranking; User group; Association rule (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s10796-014-9495-4

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