DocumentCode :
1971076
Title :
Recommending Web Service Based on User Relationships and Preferences
Author :
Min Gong ; Zhaogui Xu ; Lei Xu ; Yanhui Li ; Lin Chen
Author_Institution :
State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
fYear :
2013
fDate :
June 28 2013-July 3 2013
Firstpage :
380
Lastpage :
386
Abstract :
With the popularity of social network and the increasing number of Web Services, making individual service recommendation has been a hot research spot nowadays. In this paper, we present a service recommendation algorithm named as URPC-Rec (User Relationships & Preferences Clustering and Recommendation), which first clusters users based on their history behaviors such as the services they ever invoked, and then makes personalized recommendations for users considering both the clustering results and user basic information and relationships, such as gender, age, occupation, preference tags, etc. The case study indicates that URPC-Rec can effectively reduce the dimensionality of sparse matrix, and partially solve the cold-start problem of recommendation systems. The comprehensive experiment shows that URPC-Rec algorithm with user relationships and references has better recommending result than the one without user information and the collaborative filtering approach.
Keywords :
Web services; recommender systems; user interfaces; URPC-Rec service recommendation algorithm; cold-start problem; collaborative filtering approach; personalized recommendations; recommending Web Service; service recommendation; social network; sparse matrix dimensionality; user information; user relationships and preferences clustering and recommendation; Clustering algorithms; Collaboration; Motion pictures; Quality of service; Social network services; Training; Web services; Web service; recommendation algorithm; social network; user preference;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Services (ICWS), 2013 IEEE 20th International Conference on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5025-1
Type :
conf
DOI :
10.1109/ICWS.2013.58
Filename :
6649602
Link To Document :
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