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2017 IEEE International Conference on Web Services (ICWS) (2017)
Honolulu, Hawaii, USA
June 25, 2017 to June 30, 2017
ISBN: 978-1-5386-0752-7
pp: 220-228
Service (API) discovery and recommendation is key to the wide spread of service oriented architecture and service oriented software engineering. Service recommendation typically relies on service linkage prediction calculated by the semantic distances (or similarities) among services based on their collection of inherent attributes. Given a specific context (mashup goal), however, different attributes may contribute differently to a service linkage. In this paper, instead of training a model for all attributes as a whole, a novel approach is presented to simultaneously train separate models for individual attributes. Meanwhile, a latent attribute modeling method is developed to reveal context-aware attribute distribution. Experiments over real-world datasets have demonstrated that this fine-grained method yields higher link prediction accuracy.
Mashups, Couplings, Social network services, Context modeling, History, Predictive models, Computational modeling

Q. Bao et al., "A Fine-Grained API Link Prediction Approach Supporting Mashup Recommendation," 2017 IEEE International Conference on Web Services (ICWS), Honolulu, Hawaii, USA, 2017, pp. 220-228.
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