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2014 IEEE International Conference on Web Services (ICWS) (2014)
Anchorage, AK, USA
June 27, 2014 to July 2, 2014
ISBN: 978-1-4799-5053-9
pp: 439-446
Service compositions inherently require multiple services each with its domain-specific functionality. Therefore, how to mine matching patterns between services in relevant domains and compositions becomes crucial to service recommendation for composition. Existing methods usually overlook domain relevance and domain-specific matching patterns, which restrict the quality of recommendations. In this paper, a novel approach is proposed to offer domain-aware service recommendation. First, a K Nearest Neighbor variant (vKNN) based on topic model Latent Dirichlet Allocation (LDA) is introduced to cluster services into semantically coherent domains. On top of service domain clustering results by vKNN, a probabilistic matching model Domain Router (DR) based on Extreme Learning Machine (ELM) is developed for decomposing a requirement to relevant domains. Finally, a comprehensive Domain Topic Matching (DTM) model is built to mine relevant domain-specific matching patterns to facilitate service recommendation. Experiments on a large-scale real-world dataset show that DTM not only gains significant improvement at precision rate but also enhances the diversity of results.
Pattern matching, Vectors, Feature extraction, Predictive models, Training, Clustering methods, Clustering algorithms

B. Xia et al., "Domain-Aware Service Recommendation for Service Composition," 2014 IEEE International Conference on Web Services (ICWS), Anchorage, AK, USA, 2014, pp. 439-446.
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