一种结合显式特征和隐式特征的开发者混合推荐算法
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通讯作者:

杜军威,E-mail:djwqd@163.com

中图分类号:

TP311

基金项目:

国家自然科学基金(6217072142, 61773384, 61973180); 山东省自然科学基金(ZR2021MF092, ZR2019MF014, ZR2018MF007, ZR2019MF033); 山东省重点研发项目(2018GGX101052).


Developer Hybrid Recommendation Algorithm Based on Combination of Explicit Features and Implicit Features
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    摘要:

    现有开发者推荐算法通过对任务和开发者的显式信息进行挖掘, 抽取任务和开发者的显式特征, 完成针对任务的开发者推荐. 然而, 由于显式信息中的描述信息是主观的, 往往是不精确的, 现有基于显式特征的开发者推荐算法性能不够理想. 众包软件开发平台除包含大量不精确的描述信息外, 还包含客观的、较准确的“任务—开发者”成绩信息, 可以有效地推断任务和开发者的隐式特征. 考虑到隐式特征作为显式特征的补充, 将有效缓解描述信息不精确的难题, 提出一种结合显式特征和隐式特征的开发者混合推荐算法. 首先, 利用任务和开发者的平台可见信息充分提取显式特征, 提出面向显式特征的因子分解机(FM)推荐模型建模任务、开发者显式特征和相应评分的映射关系. 然后, 利用“任务—开发者”成绩矩阵提取隐式特征, 提出面向隐式特征的矩阵分解(MF)推荐模型. 最后, 融合面向显式特征的FM推荐模型和面向隐式特征的MF推荐模型, 提出多层感知器融合算法. 进一步, 针对冷启动问题, 首先, 基于历史数据, 构建多层感知器模型建模显式特征到隐式特征的映射关系. 然后, 针对冷启动任务或冷启动开发者, 通过任务或开发者的显式特征求解相应的隐式特征. 最后, 基于已训练好的多层感知器融合算法预测评分. 在Topcoder软件众包平台的仿真实验表明本文算法相对于对比算法在4种不同测试指标上具有明显的优势.

    Abstract:

    Existing developer recommendation algorithms extract explicit features of tasks and developers by mining the explicit information of tasks and developers, so as to recommend developers to specific tasks. However, since the description information in the explicit information is subjective and often imprecise, the performance of existing developer recommendation algorithms based on explicit features is not ideal. The crowdsourcing software development platforms not only have a lot of imprecise description information, but also contain objective and more accurate “task-developer” score information, which can effectively infer implicit features of tasks and developers. Considering that implicit features are supplements to explicit features, which will effectively alleviate the problem of imprecise description information, this study proposes a developer hybrid recommendation algorithm that combines explicit features and implicit features. First, the explicit features are fully extracted from the visible information of tasks and the developers on the platform, and the explicit features-oriented factorization machine (FM) recommendation model is proposed to learn the relationship between explicit features of tasks and developers and the corresponding ratings. Then, implicit features are inferred with the "task-developer" rating matrix, and the implicit features-oriented matrix factorization (MF) recommendation model is proposed. Finally, a multi-layer perceptron fusion algorithm is proposed to fuse the explicit features-oriented FM recommendation model and implicit features-oriented MF recommendation model. Further, for the cold-start problem, first, based on historical data, a multi-layer perceptron model is utilized to learn the mapping relationship between explicit features and implicit features. Then, for the cold-start tasks or the cold-start developers, the implicit features are obtained through their explicit features. Finally, the ratings are predicted based on the trained multi-layer perceptron fusion algorithm. The simulation experiment on the Topcoder software crowdsourcing platform shows that the proposed algorithm outperforms the comparison algorithms significantly in terms of four different evaluation metrics.

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引用本文

于旭,何亚东,杜军威,王昭哲,江峰,巩敦卫.一种结合显式特征和隐式特征的开发者混合推荐算法.软件学报,2022,33(5):1635-1651

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  • 收稿日期:2021-08-09
  • 最后修改日期:2021-10-09
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  • 在线发布日期: 2022-01-28
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