Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11): 320-329.doi: 10.11896/jsjkx.241200129

• Computer Software • Previous Articles     Next Articles

MDGRec:Multi-relation Aware Third-party Library Recommendation with Dual Graph NeuralNetworks for Mobile Application Development

CHEN Yuhan, WANG Jian, LI Duantengchuan, ZHENG Chao, LI Bing   

  1. School of Computer Science,Wuhan University,Wuhan 430072,China
  • Received:2024-12-17 Revised:2025-03-24 Online:2025-11-15 Published:2025-11-06
  • About author:CHEN Yuhan,born in 1999,postgra-duate.His main research interests include recommender systems,software engineering and service computing.
    LI Bing,born in 1969,Ph.D,professor,Ph.D supervisor,is a distinguished member of CCF(No.06539D).His main research interests include software engineering,service computing and artificial intelligence.
  • Supported by:
    Key Program of the National Natural Science Foundation of China(62032016).

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Abstract: Third-party library(TPL)recommendation systems are designed to help developers select suitable libraries,thus improving the efficiency of mobile application(App) development.Most existing methods based on graph neural networks typically propagate information for both App and TPL nodes within a single heterogeneous interaction graph,leading to issues of data imbalance and feature confusion,limiting the recommendation accuracy.Moreover,these methods often fail to account for the complex relationships inherent in the context of TPL recommendation.To overcome these limitations,this paper proposes a multi-relation aware third-party library recommendation method with dual graph neural networks for mobile application development(MDGRec).The model employs a dual graph structure to separately model Apps and TPLs,generating distinct embeddings.Based on this,the model incorporates multiple relationships and uses adaptive weights to capture the contribution of each relation in information propagation,constructing fine-grained node representations.Experimental results on two real-world datasets show that the proposed model surpasses mainstream baselines across all metrics.

Key words: Recommender systems, Graph neural network, Multi-relation, Third-party library, Mobile application

CLC Number: 

  • TP311
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