计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 106-114.doi: 10.11896/jsjkx.211200105
刘泽京1, 邬楠1, 黄抚群2, 宋友1
LIU Zejing1, WU Nan1, HUANG Fuqun2, SONG You1
摘要: 在线编程评测系统 (Online Judge,OJ)是一种被广泛应用于计算机编程教学与竞赛的代码测评系统。用户在规模庞大的题库中寻找适合当前学习阶段的题目时,往往会感到迷茫。如何为用户推荐合适的题目和规划学习路径,是在线编程测评系统研发中的一个重要研究课题。传统推荐算法存在可解释性和准确性难以兼顾的问题。文中提出了基于知识图谱与协同过滤混合策略的在线评测系统推荐模型(A Hybrid Programming Task Recommendation Model Based on Knowledge Graph and Collaborative Filtering,HKGCF)。该模型通过推荐与用户当前知识和技能掌握程度相匹配的题目,来帮助用户提升学习效果。文中设计和实现了该模型,并将其集成到了北京航空航天大学在线编程测评系统中,以适应OJ平台特有的交互形式。线上测试和离线测试实验的结果表明,提出的HKGCF模型在准确率和可解释性方面均优于典型传统算法。
中图分类号:
[1]MIRZAYANOV M,PAVLOVA O,MAVRIN P,et al.Code-forces as an Educational Platform for Learning Programming in Digitalization [J].Olympiads in Informatics,2020,14(10):133-142. [2]LI W X,GUO W.Peking university oneline judge and its applications [J].Journal of Changchun Post and Telecommunication Institute,2005(S2):170-177. [3]PARK Y.Predicting personalized student performance in computing-related majors via collaborative filtering[C]//Procee-dings of the 19th Annual SIG Conference on Information Technology Education.Florida:ACM Press,2018:151-151. [4]RECHKOSKI L,AJANOVSKI V V,MIHOVA M.Evaluationof grade prediction using model-based collaborative filtering methods[C]//2018 IEEE Global Engineering Education Confe-rence(EDUCON).Santa Cruz de Tenerife:IEEE Press,2018:1096-1103. [5]SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques [J].Advances in artificial intelligence,2009,2009(1):1-19. [6]WU Q,HUANG M,MU Y.A Collaborative Filtering Algo-rithm Based on User Similarity and Trust [C]//2017 14th Web Information Systems and Applications Conference(WISA).Liuzhou:IEEE Press,2017:263-266. [7]YU X,CHEN W.Research on three-layer collaborative filtering recommendation for Online Judge [C]//2016 Seventh International Green and Sustainable Computing Conference (IGSC).Hangzhou:IEEE Press,2016:1-4. [8]HE M,SUN W,XIAO R.A collaborative filtering recommenda-tion algorithm fusing clustering and user interest preferences [J].Computer Science,2017,44(S2):391-396. [9]HE M,XIAO R,LIU W S,et al.Collaborative filtering recommendation algorithm fusing category information and user inte-rest degree [J].Computer Science,2017,44(8):230-235,269. [10]KIM B H,VIZITEI E,GANAPATHI V.GritNet:Student performance prediction with deep learning[J].arXiv:1804.07405,2018. [11]SAITO T,WATANOBE Y.Learning Path RecommendationSystem for Programming Education Based on Neural Networks [J].international journal of distance education technologies,2020,18(1):36-64. [12]XIAO J,YE H,HE X,et al.Attentional factorization machines:Learning the weight of feature interactions via attention networks[J].arXiv:1708.04617,2017. [13]ZHOU G,ZHU X,SONG C,et al.Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.London:ACM Press,2018:1059-1068. [14]CHOUNTA I A,CARVALHO P F.Square it up! How to mo-del step duration when predicting student performance[C]//Proceedings of the 9th International Conference on Learning Analytics & Knowledge.Tempe:ACM Press,2019:330-334. [15]LIANG H H,GU T L,BING C Z,et al.Joint learning of user-side and project-side knowledge graphs for personalized recommendation [J].Computer Science,2021,48(5):109-116. [16]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[J].Advances in neural information processing systems,2013,26(1):2787-2795. [17]WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Quebec City:AAAI Press,2014,28(1):1112-1119. [18]CHEN X J,XIANG Y.STransH:An improved knowledge representation model based on translation model [J].Computer Science,2019,46(9):184-189. [19]WANG H,ZHANG F,ZHAO M,et al.Multi-task feature lear-ning for knowledge graph enhanced recommendation[C]//The World Wide Web Conference.San Francisco:ACM Press,2019:2000-2010. [20]PALUMBO E,MONTI D,RIZZO G,et al.entity2rec:Property-specific knowledge graph embeddings for item recommendation[J].Expert Systems with Applications,2020,151(15):1-18. [21]ZHANG F,YUAN N J,LIAN D,et al.Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco:ACM Press,2016:353-362. [22]KANG Y,LI T,LI H,et al.A recommendation model combining knowledge graph and collaborative filtering [J].Computer Engineering,2020,46(12):73-79,87. [23]JI G,LIU K,HE S,et al.Knowledge graph completion withadaptive sparse transfer matrix[C]//Thirtieth AAAI Conference on Artificial Intelligence.Phoenix:AAAI Press,2016:985-991. [24]GLOROT X,BENGIO Y.Understanding the difficulty of trai-ning deep feedforward neural networks[C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics.JMLR Workshop and Conference Proceedings,2010:249-256. |
[1] | 白雪飞, 马亚楠, 王文剑. 基于特征融合的边缘引导乳腺超声图像分割方法 Segmentation Method of Edge-guided Breast Ultrasound Images Based on Feature Fusion 计算机科学, 2023, 50(3): 199-207. https://doi.org/10.11896/jsjkx.211200294 |
[2] | 谢秦秦, 何朗, 徐汝利. 基于多特征融合的油画艺术风格分类 Classification of Oil Painting Art Style Based on Multi-feature Fusion 计算机科学, 2023, 50(3): 223-230. https://doi.org/10.11896/jsjkx.211200110 |
[3] | 马廷淮, 孙圣杰, 荣欢, 钱敏峰. 基于动态记忆和双层重构强化的知识图谱至文本转译模型 Knowledge Graph-to-Text Model Based on Dynamic Memory and Two-layer Reconstruction Reinforcement 计算机科学, 2023, 50(3): 12-22. https://doi.org/10.11896/jsjkx.220700111 |
[4] | 汪璟玢, 赖晓连, 林新宇, 杨心逸. 基于关系约束的上下文感知时态知识图谱补全 Context-aware Temporal Knowledge Graph Completion Based on Relation Constraints 计算机科学, 2023, 50(3): 23-33. https://doi.org/10.11896/jsjkx.220400255 |
[5] | 陈富强, 寇嘉敏, 苏利敏, 李克. 基于图神经网络的多信息优化实体对齐模型 Multi-information Optimized Entity Alignment Model Based on Graph Neural Network 计算机科学, 2023, 50(3): 34-41. https://doi.org/10.11896/jsjkx.220700242 |
[6] | 刘昕炜, 陶传奇. 一种静态分析与知识图谱结合的Java冗余代码检测方法 Method of Java Redundant Code Detection Based on Static Analysis and Knowledge Graph 计算机科学, 2023, 50(3): 65-71. https://doi.org/10.11896/jsjkx.220700240 |
[7] | 陈姝睿, 梁子然, 饶洋辉. 细粒度语义知识图谱增强的中文OOV词嵌入学习 Fine-grained Semantic Knowledge Graph Enhanced Chinese OOV Word Embedding Learning 计算机科学, 2023, 50(3): 72-82. https://doi.org/10.11896/jsjkx.220700249 |
[8] | 蒋川宇, 韩翔宇, 杨文蕊, 吕博涵, 黄小欧, 谢夏, 谷阳. 医学知识图谱研究与应用综述 Survey of Medical Knowledge Graph Research and Application 计算机科学, 2023, 50(3): 83-93. https://doi.org/10.11896/jsjkx.220700241 |
[9] | 李志飞, 赵月, 张龑. 基于表示学习的知识图谱推理研究综述 Survey of Knowledge Graph Reasoning Based on Representation Learning 计算机科学, 2023, 50(3): 94-113. https://doi.org/10.11896/jsjkx.220900136 |
[10] | 章琪, 于双元, 尹鸿峰, 徐保民. 基于图注意力的神经协同过滤社会推荐算法 Neural Collaborative Filtering for Social Recommendation Algorithm Based on Graph Attention 计算机科学, 2023, 50(2): 115-122. https://doi.org/10.11896/jsjkx.211200019 |
[11] | 邹芸竹, 杜圣东, 滕飞, 李天瑞. 一种基于多模态深度特征融合的视觉问答模型 Visual Question Answering Model Based on Multi-modal Deep Feature Fusion 计算机科学, 2023, 50(2): 123-129. https://doi.org/10.11896/jsjkx.211200303 |
[12] | 华杰, 刘学亮, 赵烨. 基于特征融合的小样本目标检测 Few-shot Object Detection Based on Feature Fusion 计算机科学, 2023, 50(2): 209-213. https://doi.org/10.11896/jsjkx.220500153 |
[13] | 单中原, 杨恺, 赵俊峰, 王亚沙, 徐涌鑫. 一种增量式本体模型与数据模式映射的图谱实例模型构建演化方法 Ontology-Schema Mapping Based Incremental Entity Model Construction and Evolution Approach of Knowledge Graph 计算机科学, 2023, 50(1): 18-24. https://doi.org/10.11896/jsjkx.220500205 |
[14] | 郝敬宇, 文静轩, 刘华锋, 景丽萍, 于剑. 结合全局信息的深度图解耦协同过滤 Deep Disentangled Collaborative Filtering with Graph Global Information 计算机科学, 2023, 50(1): 41-51. https://doi.org/10.11896/jsjkx.220900255 |
[15] | 赵倩, 周冬明, 杨浩, 王长城. 残差注意力与多特征融合的图像去模糊 Image Deblurring Based on Residual Attention and Multi-feature Fusion 计算机科学, 2023, 50(1): 147-155. https://doi.org/10.11896/jsjkx.211100161 |
|