论文标题
通过隐式反馈提高API建议
Boosting API Recommendation with Implicit Feedback
论文作者
论文摘要
开发人员通常需要使用适当的API进行有效的编程，但是通常很难从许多候选人中确定所需的API。为了减轻负担，已经提出了多种API建议方法。但是，大多数当前可用的API推荐人不支持将用户反馈的有效整合到建议循环中。在本文中，我们提出了一个框架，辫子（通过隐式反馈来提高建议），该框架利用学习对象和积极的学习技术来提高建议性能。通过利用用户的反馈信息，我们培训了一个学习到级的模型，以重新排列建议结果。此外，我们通过主动学习加快了反馈学习过程。现有的基于查询的API建议方法可以插入编织中。我们选择三种最先进的API推荐方法作为基准，以证明由hit@k（Top-K），地图和MRR测量的编织的性能增强。经验实验表明，通过可接受的开销，随着反馈数据比例的增加，建议性能稳定而大大提高，与基准相比。
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 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 users' feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting users' 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.
