计算机科学与探索2024，Vol.18Issue(8)：2049-2064,16.DOI:10.3778/j.issn.1673-9418.2305069
基于PathSim的MOOCs知识概念推荐模型
MOOCs Knowledge Concept Recommendation Model Based on PathSim
摘要
Abstract
Massive open online courses play a crucial role in advancing modern education by providing extensive open online learning platforms.However,there are still challenging aspects to consider when it comes to reducing user learning blind spots and improving the overall user experience.Firstly,interaction data are sparse.Secondly,scaling up to large-scale recommendation tasks is difficult.Thirdly,user needs are not solely determined by individual preferences,but are also influenced by different teachers and course materials.Fourthly,developing a unified model that can effectively represent different types of entities and relationships within course learning events is a challenging task.This paper introduces a relevance metric that computes the weights of edges by leveraging the structural infor-mation of entire graph.This paper presents the PathSimSage model(path-based similarity sampler and aggregate)for recommending knowledge concepts,utilizing the PathSim algorithm(path-based similarity)for neighborhood sampling.The relevance scores between entities are precomputed offline,which decouples the neural network from the propagation process.This decoupling maintains the independence of the network's layered architecture from the prop-agation mechanism,thereby considerably reducing the training time of model.Through extensive experimentation on the publicly accessible MoocCube dataset,PathSimSage has shown to minimize the impact of irrelevant or noisy infor-mation,resolve the significant node bias induced by random walk sampling,and somewhat alleviate the issue of oversmoothing.关键词
大规模开放在线课程/图神经网络/个性化课程推荐/图卷积/基于元路径的子图/相似性度量Key words
massive open online courses/graph neural networks/personalized course recommendations/graph con-volution/metapath-based subgraphs/similarity measure分类
信息技术与安全科学引用本文复制引用
祝义,居程程,郝国生..基于PathSim的MOOCs知识概念推荐模型[J].计算机科学与探索,2024,18(8):2049-2064,16.基金项目
国家自然科学基金(62077029,62277030) （62077029,62277030）
CCF-华为创新研究计划(CCF-HuaweiFM202209) （CCF-HuaweiFM202209）
南京航空航天大学基本科研业务费科研基地创新基金(NJ2020022). This work was supported by the National Natural Science Foundation of China(62077029,62277030),the CCF-Huawei Populus Grove Fund(CCF-HuaweiFM202209),and the Innovation Foundation of Fundamental Research Funds of Nanjing University of Aero-nautics and Astronautics(NJ2020022). （NJ2020022）
