基于多关系知识增强的开发者推荐算法

杜军威; 王昭哲; 于旭; 胡强; 江峰; 巩敦卫

doi:10.12263/DZXB.20230271

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基于多关系知识增强的开发者推荐算法

学术论文 | 更新时间：2025-12-08

- 基于多关系知识增强的开发者推荐算法
- A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement
- 电子学报 2023年51卷第11期页码：3111-3119
- 作者机构：
  
  青岛科技大学信息科学技术学院，山东青岛 266061
- 作者简介：
  
  [ "杜军威男，1974年7月出生，山东威海人.博士，教授，CCF高级会员，博士生导师.主要研究方向为智能软件工程、推荐算法和自然语言处理. E-mail: djwqd@163.com" ]
  [ "王昭哲男，1997年1月出生，河南商丘人.现为硕士研究生.主要研究方向为推荐系统.E-mail: wzz9701@163.com" ]
  [ "于旭（通讯作者）男，1982年7月出生，山东青岛人.博士，教授，CCF高级会员.主要研究推荐算法、迁移学习、智能软件工程." ]
  [ "胡强男，1980年6月出生，山东邹城人.博士，副教授，硕士生导师.主要研究方向为服务计算、人工智能. Email: huqiang200280@163.com" ]
  [ "江峰男，1978年10月出生，江西彭泽人.博士，教授，CCF专业会员.主要研究领域为机器学习，缺陷预测.E-mail: jiangkong2002@163.com" ]
  [ "巩敦卫男，1970 年3月出生，江苏徐州人.博士，教授，CCF杰出会员.主要研究智能优化与控制、基于搜索的软件工程. E-mail: dwgong@vip.163.com" ]
- 基金信息：
  
  国家自然科学基金(62172249;61973180);山东省自然科学基金(ZR2021MF092;ZR2022MF326);中央高校基本科研业务费(93K172022K01)
- DOI：10.12263/DZXB.20230271
  中图分类号： TP311.5
- 收稿：2023-03-28，
  
  修回：2023-09-22，
  
  纸质出版：2023-11-25
- 稿件说明：
移动端阅览
杜军威,王昭哲,于旭等.基于多关系知识增强的开发者推荐算法[J].电子学报,2023,51(11):3111-3119.

DU Jun-wei,WANG Zhao-zhe,YU Xu,et al.A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement[J].ACTA ELECTRONICA SINICA,2023,51(11):3111-3119.
杜军威,王昭哲,于旭等.基于多关系知识增强的开发者推荐算法[J].电子学报,2023,51(11):3111-3119. DOI： 10.12263/DZXB.20230271.

DU Jun-wei,WANG Zhao-zhe,YU Xu,et al.A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement[J].ACTA ELECTRONICA SINICA,2023,51(11):3111-3119. DOI： 10.12263/DZXB.20230271.

摘要

近年来，随着众包平台的不断发展，信息过载问题日趋严重，任务难以及时找到可靠的开发者完成，为任务推荐合适的开发者变得至关重要.传统推荐方法存在两大挑战：一是任务和开发者的文本特征高度简练，传统推荐方法聚焦于表面文本信息，未发现其中包含的大量知识实体；二是任务具有一次性，导致显式交互数据极其稀疏.为了解决上述挑战，本文提出一种基于多关系知识增强的开发者推荐算法.对于一个任务和开发者，首先将他们包含的文本内容中的每个单词与知识图谱中的相关实体关联起来，用于丰富任务和开发者的信息表示.除直接相关联的实体外，还使用每个实体的上下文实体集合来提供更多的信息.然后，对于开发者本文使用多关系邻域聚合的方式增强其特征表示，并使用注意力模块区分开发者对任务的关注度.最终获得的用户和开发者的嵌入输入到深度神经网络中进行预测.在真实的Topcoder数据集上进行广泛的实验，结果表明，本文方法在正确率和序位倒数均值上相比于最佳对比方法平均提高11.7%和17.5%.

Abstract

In recent years

crowdsourcing software development has gradually become an emerging software development model. However

with the continuous development of crowdsourcing platforms

the problem of information overload has become increasingly serious. It has become crucial to recommend suitable developers for tasks that are difficult to find reliable developers to complete on time. Traditional recommendation methods face two major challenges: First

the text features of tasks and developers are highly concise. Traditional recommendation methods focus on surface text information and fail to discover the large amount of knowledge entities contained therein. Second

tasks are one-time and this results in extremely sparse explicit interaction data. To solve these challenges

we propose a developer recommendation algorithm based on multi-relational knowledge enhancement. We identify entities and contextual entities from text features and link the relationship between tasks and developers from a knowledge perspective

uncovering the deep preferences of developers. In addition

we treat developers' participation in registering

submitting

and winning tasks as different preferences. We assign different weights to the relationships between tasks and developers and employ an attention mechanism to refine the importance of these different relationships. Finally

we enhance developers' feature representation using multi-relational neighborhood aggregation. We conduct extensive experiments on the real-world Topcoder dataset

and the results show that our method outperforms the best baseline method by an average of 11.7% in accuracy and 17.5% in mean reciprocal rank.

关键词

Keywords

references

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