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    Boosting API recommendation with implicit feedback

    Zhou, Y. and Yang, X. and Chen, Taolue and Huang, Z. and Ma, X. and Gall, H.C. (2022) Boosting API recommendation with implicit feedback. IEEE Transactions on Software Engineering 48 (6), pp. 2157-2172. ISSN 0098-5589.

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    Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast list of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we propose a framework, BRAID ( B oosting R ecommend A tion with I mplicit Fee D back), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting user feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Taolue Chen
    Date Deposited: 30 Oct 2023 16:24
    Last Modified: 31 Oct 2023 08:12


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