Abstract
Web services are products in the era of service-oriented computing and cloud computing. Considering the information overload problem arising from the task of selecting web services, a recommendation system is by far the most effective solution for performing such selections. However, users calling a limited number of services will cause severe data sparseness and a weak correlation with services. In addition, fully mining the semantic features and knowledge features of the text description is also a major problem that needs to be solved urgently. This paper proposes a deep knowledge-aware approach which introduces knowledge graph and knowledge representation into web service recommendation for the first time. We solve the data sparse problem and optimize the user’s feature representation. In this approach, an attention module is introduced to model the impact of tags for the candidate services on different words of user queries, and a deep neural network is used to model the high-level features of user-service invocation behaviors. The results of experiments demonstrate that the proposed approach can achieve better recommendation performance than other state-of-the-art methods.







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Acknowledgements
This research is supported by the National Natural Science Foundation of China under Grant No. 61672102, No. 61073034, No. 61370064 and No. 60940032; the National Social Science Foundation of China under Grant No. BCA150050; and the Program for New Century Excellent Talents in the University of Ministry of Education of China under Grant No. NCET-10-0239.
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Dang, D., Chen, C., Li, H. et al. Deep knowledge-aware framework for web service recommendation. J Supercomput 77, 14280–14304 (2021). https://doi.org/10.1007/s11227-021-03832-2
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DOI: https://doi.org/10.1007/s11227-021-03832-2