Abstract
The number of APIs on the Web has increased rapidly in recent years. It becomes quite popular for developers to combine different APIs to build innovative Mashup applications. However, it is challenging to discover the appropriate ones from enormous APIs for Mashup developers (i.e., API users). In order to recommend a set of APIs that most satisfy the users’ requirements, we propose a multi-relation based manifold ranking approach. The approach exploits the textual descriptions of existing Mashups and APIs, as well as their composition relationships. It firstly groups Mashups into different clusters according to their textual descriptions, then explores multiple relations between Mashup clusters and between APIs. Finally, it employs a manifold ranking algorithm to recommend appropriate APIs to the user. Experiments on a real-world dataset crawled from ProgrammableWeb.com validate the effectiveness of the proposed approach.
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Acknowledgments
The work described in this paper was supported by the National Natural Science Foundation of China under grant No. 61572186, 61572187, 61402168 and 61300129, Scientific Research Fund of Hunan Provincial Education Department of China under grant 15K043, 16K030, Hunan Provincial University Innovation Platform Open Fund Project of China under grant No. 14K037.
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Xie, F., Liu, J., Tang, M., Zhou, D., Cao, B., Shi, M. (2016). Multi-relation Based Manifold Ranking Algorithm for API Recommendation. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_2
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DOI: https://doi.org/10.1007/978-3-319-49178-3_2
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