A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement

DU Jun-wei; WANG Zhao-zhe; YU Xu; HU Qiang; JIANG Feng; GONG Dun-wei

doi:10.12263/DZXB.20230271

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A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement

PAPERS | 更新时间：2025-12-08

- A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3111-3119(2023)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62172249;61973180);Natural Science Foundation of Shandong Province(ZR2021MF092;ZR2022MF326);Basic Research Fund for Central Universities(93K172022K01)
- DOI：10.12263/DZXB.20230271
  CLC： TP311.5
- Received：28 March 2023，
  
  Revised：2023-09-22，
  
  Published：25 November 2023
- 稿件说明：
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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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