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Type: Conference paper
Title: Recommending web services via combining collaborative filtering with content-based features
Author: Yao, L.
Sheng, Q.
Segev, A.
Yu, J.
Citation: Proceedings, IEEE 20th International Conference on Web Services, ICWS 2013: pp.42-49
Publisher: IEEE
Publisher Place: Online
Issue Date: 2013
ISBN: 9780769550251
Conference Name: IEEE International Conference on Web Services (20th : 2013 : Santa Clara, California)
Statement of
Lina Yao and Quan Z. Sheng, Aviv Segev, Jian Yu
Abstract: With increasing adoption and presence of Web services, designing novel approaches for efficient Web services recommendation has become steadily more important. Existing Web services discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant Web service search engines, which possess many limitations such as insufficient recommendation performance and heavy dependence on the input from users such as preparing complicated queries. In this paper, we propose a novel approach that dynamically recommends Web services that fit users' interests. Our approach is a hybrid one in the sense that it combines collaborative filtering and content-based recommendation. In particular, our approach considers simultaneously both rating data and content data of Web services using a three-way aspect model. Unobservable user preferences are represented by introducing a set of latent variables, which is statistically estimated. To verify the proposed approach, we conduct experiments using 3, 693 real-world Web services. The experimental results show that our approach outperforms the two conventional methods on recommendation performance.
Keywords: Web service recommendation; collaborative filtering; content-based recommendation; three-way aspect model
Rights: © 2013 IEEE
RMID: 0020134785
DOI: 10.1109/ICWS.2013.16
Appears in Collections:Computer Science publications

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