Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (2): 106-114.doi: 10.11896/jsjkx.211200105

• Database & Big Data & Data Science • Previous Articles     Next Articles

Hybrid Programming Task Recommendation Model Based on Knowledge Graph and Collaborative Filtering for Online Judge

LIU Zejing1, WU Nan1, HUANG Fuqun2, SONG You1   

  1. 1 School of Software,Beihang University,Beijing 100191,China
    2 Centre for Informatics andSystems,University of Coimbra,Coimbra 3000-115,Portugal
  • Received:2021-12-09 Revised:2022-07-13 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    National Natural Science Foundation of China General Program(61977002),National Natural Science Foundation of China for Youths(62107002) and key Research and Development Program of Hebei Province(21310101D)

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Abstract: The online judge (OJ) is a widely used system for programming education,learning and contests.Users often get lost in searching for tasks of interest in the massive database.How to recommend suitable programming tasks to the users and plan the learning path is a significant research topicin the development of online programming evaluation system.Existing traditional recommendation methods have the limitation of making a trade-off between interpretability and effectiveness.This paper proposes a task-recommending model for the OJ platform -hybrid programming task recommendation model based on knowledge graph and collaborative filtering for online judge (HKGCF).The HKGCF model can help users improve their learning effect by recommending questions that match their current knowledge levels and skills.The model is designed based on a hybrid strategy that integrates the knowledge graph representation learning with an improved collaborative filtering algorithm.The model is implemented and integrated into the OJ platform of Beihang University,and meet the specific interaction formats of the OJ platform.We conducted two experiments,an online and an offline test,to validate the proposed model and its implementations.The results show that the proposed model outperforms the representative conventional recommendation algorithm interms of interpretability and accuracy

Key words: Programming education, Online judge systems, Personalized recommendations, Knowledge graph, Collaborative filtering, Feature fusion

CLC Number: 

  • TP391
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