Collaborative personal profiling for web service ranking and recommendation

被引：0
作者：
Wenge Rong
Baolin Peng
Yuanxin Ouyang
Kecheng Liu
Zhang Xiong
机构：
[1] Beihang University,State Key Laboratory of Software Development Environment
[2] Beihang University,School of Computer Science and Engineering
[3] Research Institute of Beihang University in Shenzhen,Informatics Research Centre
[4] University of Reading,School of Information Management and Engineering
[5] Shanghai University of Finance and Economics,undefined
来源：
Information Systems Frontiers | 2015年 / 17卷
关键词：
Web service; Discovery; Personalisation; Ranking; User group; Association rule;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：1265 / 1282
页数：17
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