Abstract
Currently, service tag recommendation plays an important role in the study of services. As a result, there have been many service tag recommendation studies that have achieved significant achievements. However, existing studies mainly have two problems: they only recommend one tag and cannot determine whether new tags are needed. To help solve the above problems, we propose a novel graph neural framework named TagTag to make multi-tag recommendations and missing tag prediction, which relies on the idea of tag collaboration graph. We conduct experiments on the real-world dataset from ProgrammableWeb, and the results show that TagTag performs better than existing studies. The code used in this paper is fully accessible at https://github.com/HIT-ICES/TagTag.
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Acknowledgement
The research in this paper is partially supported by the National Key Research and Development Program of China (No 2021YFB3300700) and the National Natural Science Foundation of China (61832014, 61832004).
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Chen, W., Liu, M., Tu, Z., Wang, Z. (2022). TagTag: A Novel Framework for Service Tags Recommendation and Missing Tag Prediction. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernández, P., Ruiz-Cortés, A. (eds) Service-Oriented Computing. ICSOC 2022. Lecture Notes in Computer Science, vol 13740. Springer, Cham. https://doi.org/10.1007/978-3-031-20984-0_24
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DOI: https://doi.org/10.1007/978-3-031-20984-0_24
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