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Edge-cloud-enabled matrix factorization for diversified APIs recommendation in mashup creation

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Abstract

A growing number of web APIs published on the Internet allows mashup developers to discover appropriate web APIs for polishing mashups. Developers often have to manually pick and choose several web APIs from extremely massive candidates, which is a laborious and cumbersome task. Fortunately, recommender system comes into existence. Some approaches perform recommendations in cloud platforms by utilizing historical records of Mashup-API interactions stored in edge nodes. However, many of these methods often pay more attention to recommendation accuracy while ignoring recommendation diversity, i.e., there are usually popular web APIs in recommendation list while most of the other novel web APIs are absent. The poor recommendation diversity may limit the usefulness of the recommendation results due to the lack of novelty. In order to implement an accurate and diversified web API recommendation, a novel MF-based recommendation approach named Div_PreAPI is put forward in this paper. Div_PreAPI integrates a weighting mechanism and neighborhood information into matrix factorization (MF) to implement diversified and personalized APIs recommendations. Finally, we conduct a series of experiments on a real-world dataset. Experimental results show the effectiveness of our proposal.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61872219) and the Natural Science Foundation of Shandong Province (ZR2019MF001).

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Authors and Affiliations

  1. School of Computer Science and Technology, Qufu Normal University, Rizhao, China

    Fan Wang, Guangshun Li, Yilei Wang & Lianyong Qi

  2. School of Computer Science and Technology, Shandong University, Jinan, China

    Lina Wang

  3. China Telecom Smart Home Competence Center, Nanjing, China

    Chao Lv

  4. E-surfing Smart Home Technology Co., Nanjing, China

    Chao Lv

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  1. Fan Wang
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  2. Lina Wang
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  3. Guangshun Li
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Correspondence to Lianyong Qi.

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This article belongs to the Topical Collection: Special Issue on Resource Management at the Edge for Future Web, Mobile and IoT Applications

Guest Editors: Qiang He, Fang Dong, Chenshu Wu, and Yun Yang

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Wang, F., Wang, L., Li, G. et al. Edge-cloud-enabled matrix factorization for diversified APIs recommendation in mashup creation. World Wide Web 25, 1809–1829 (2022). https://doi.org/10.1007/s11280-021-00943-x

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  • DOI: https://doi.org/10.1007/s11280-021-00943-x

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