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Content Provider | IEEE Xplore Digital Library |
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Author | Yang Zhong Yushun Fan Keman Huang Wei Tan Jia Zhang |
Copyright Year | 2014 |
Description | Author affiliation: IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA (Keman Huang; Wei Tan) || Dept. of Autom., Tsinghua Univ., Beijing, China (Yang Zhong; Yushun Fan) || Carnegie Mellon Univ., Pittsburgh, PA, USA (Jia Zhang) |
Abstract | Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques such as keyword search and semantic matching while others are based on Quality of Service (QoS) prediction. However, services and their mashups are evolving over time with publishing, perishing and changing of interfaces. Therefore, a practical service recommendation approach should take into account the evolution of a service ecosystem. In this paper, we present a method to extract service evolution patterns by exploiting Latent Dirichlet Allocation (LDA) and time series prediction. A time-aware service recommendation framework for mashup creation is presented combing service evolution, collaborative filtering and content matching. Experiments on real-world ProgrammableWeb data set show that our approach leads to a higher precision than traditional collaborative filtering and content matching methods. |
Starting Page | 25 |
Ending Page | 32 |
File Size | 433150 |
Page Count | 8 |
File Format | |
e-ISBN | 9781479950546 |
DOI | 10.1109/ICWS.2014.17 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-06-27 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Mashups Ecosystems Equations Mathematical model Collaboration Filtering Quality of service mashup creation service recommendation LDA service ecosystem time-aware |
Content Type | Text |
Resource Type | Article |
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