Computer Science ›› 2022, Vol. 49 ›› Issue (8): 64-69.doi: 10.11896/jsjkx.210600111

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

Hierarchical Granulation Recommendation Method Based on Knowledge Graph

QIN Qi-qi, ZHANG Yue-qin, WANG Run-ze, ZHANG Ze-hua   

  1. College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China
  • Received:2021-04-06 Revised:2021-06-13 Published:2022-08-02
  • About author:QIN Qi-qi,born in 1996,postgraduate.Her main research interests include recom-mendation system and so on.
    ZHANG Yue-qin,born in 1963,professor,master supervisor,is a member of China Computer Federation.Her main research interests include data mining,intelligent information processing and knowledge discovery on graph.
  • Supported by:
    National Natural Science Foundation of China(61503273,61702356),Industry-University Cooperation Education Program of the Ministry of Education and Shanxi Scholarship Council of China.

Abstract: The recommendation system based on graph neural network is the current research hotspot of data mining applications.The recommendation performance can be improved by combining the graph neural network on the heterogeneous information network(HIN).However,the existing HIN-based recommendation methods often have problems that cannot effectively explain the results of high-level modeling,and manual design of meta-paths requires knowledge of related domains.Therefore,this paper combines the idea of hierarchical granulation andproposes a heterogeneous recommendation method(HKR) based on knowledge graphs.The local context and non-local context are hierarchically granulated,and the coarse-grained representation of user characteristics is learned separately.Then based on the gating mechanism, combining local and non-local attribute node embedding,learning the potential features between users and items,and finally fusing fine-grained features for recommendation.The real experimental results show that the performance of the proposed method is better than the current graph neural network recommendation method based on knowledge graph in many aspects.

Key words: Hierarchical granulation, Knowledge graph, Multi-granularity fusion, Recommendation system

CLC Number: 

  • TP391
[1]FOGELMAN-SOULIE F,MEI L,J ZHANG J,et al.Recommender Systems and Attributed Networks[M].John Wiley & Sons,Ltd,2020:4.
[2]YU X,REN X,SUN Y Z,et al.Personalized entity recommendation:A heterogeneous information network approach[C]//WSDM.2014:283-292.
[3]ZHAO H,YAO Q M,LI J D,et al.Metagraph-based recommendation fusion over heterogeneous information networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017.
[4]AI Q Y,AZIZI V,CHEN X,et al.Learning HeterogeneousKnowledge Base Embeddings for Explainable Recommendation[J].Algorithms,2018,11(9):137.
[5]CAO Y X,WANG X,HE X N,et al.Unifying Knowledge Graph Learning and Recommendation:Towards a Better Understan-ding of User Preferences[C]//WWW’19.ACM,2019:151-161.
[6]SUN Z,GUO Q,YANG J,et al.Research commentary on reco-mmendations with side information:A survey and research directions[J].Electronic Commerce Research and Applications,2019,37:100879.
[7]WANG X,WANG D X,XU C R,et al.Explainable Reasoningover Knowledge Graphs for Recommendation[C]//AAAI.2019.
[8]HU B B,SHI C,ZHAO W X,et al.Leveraging Meta-path-based Context for Top-N Recommendation with A Neural Co-Attention Model[C]//SIGKDD.2018:1531-1540.
[9]YU X,REN X,GU Q Q,et al.Collaborative filtering with entity similarity regularization in heterogeneous information networks[C]//IJCAI 27.2013.
[10]LIU Q,LI Y,DUAN H,et al.Knowledge graph construction techniques[J]. Journal of Computer Research and Development,2016,53(3):582-600.
[11]DODWAD P R,LOBO L.A context-aware recommender system using ontology based approach for travel applications[J].International Journal of Advanced Engineering and Nano Techno-logy,2014,1(10):8-12.
[12]MORENO A,VALLS A,ISERN D,et al.SigTur/E-destination:Ontology based personalized recommendation of tourism and leisure activities[J].Engineering Applications of Artificial Intelligence,2013,26(1):633-651.
[13]DI NOIA T,MIRIZZI R,OSTUNI V C,et al.Linked open data to support content-based recommender systems[C]//Procee-dings of the 8th International Conference on Semantic Systems.Graz,Austria:ACM,2012.
[14]ZADAH L A.Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J].Fuzzy Sets and Systems,1997,90(2):111-127.
[15]YAO Y Y,NING Z.Potential Applications of Granular Computing in Knowledge Discovery and Data Mining[C]//Proceedings of World Multiconference on Systemics, Cybernetics and Informatics.1999:573-580.
[16]ZHANG B,ZHANG L.The Theory and Application of Problem Solving.[M].Beijing:Tsinghua University Press,1990.
[17]PEDRYCZ W.Granular Computing:An Emerging paradigm.Hardcover[M].Physica-Verlag,2001.
[18]MIAO D Q,WANG G Y,LIU Q,et al.Granular Computing:Past,Present and Prospects[M].Beijing:Science Press,2007.
[19]WU W Z,ZHANG W X.Neighborhood operator systems and approximations [J].Information Sciences,2002,114(1/2/3/4):201-217.
[20]QIAN Y,LIANG X,WANG al.Local rough set:a solution to rough data analysis in big data[J].International Journal of Approximate Reasoning,2018,97:38-63.
[21]ZHANG F Z,YUAN N J,LIAN D,et al.Collaborative Know-ledge Base Embedding for Recommender Systems[C]//KDD’16.ACM,2016:353-362.
[22]LIN Y K,LIU Z Y,SUN M S,et al.Learning entity and relation embeddings for knowledge graph completion[C]//AAAI.2015:2181-2187.
[23]WANG H W,ZHANG F Z,WANG J L,et al.RippleNet:Propagating User Preferences on the Knowledge Graph for Recommender Systems[C]//CIKM.2018:417-426.
[24]WANG H W,ZHANG F Z,ZHAO M,et al.Multi-task feature learning for knowledge graph enhanced recommendation[C]//WWW’19.ACM,2019:2000-2010.
[25]KIPF T N,WELLING M.Semi-Supervised Classification with Graph Convolutional Networks[C]//ICLR’17.2017.
[26]VELICKOVIC P,CUCURULL G,CASANOVA A,et al.Graph Attention Networks[C]//ICLR.2018.
[27]HAMILTON W L,YING R,LESKOVEC J.Inductive Representation Learning on Large Graphs[J].arXiv:1706.02216,2017.
[28]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//NeurIPS.2013:2787-2795.
[29]WANG H W,ZHANG F Z,ZHANG M D,et al.Knowledge graph convolutional networks for recommender systems with label smoothness regularization[C]//KDD’19.2019.
[1] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[2] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[3] WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220.
[4] KONG Shi-ming, FENG Yong, ZHANG Jia-yun. Multi-level Inheritance Influence Calculation and Generalization Based on Knowledge Graph [J]. Computer Science, 2022, 49(9): 221-227.
[5] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[6] SHUAI Jian-bo, WANG Jin-ce, HUANG Fei-hu, PENG Jian. Click-Through Rate Prediction Model Based on Neural Architecture Search [J]. Computer Science, 2022, 49(7): 10-17.
[7] QI Xiu-xiu, WANG Jia-hao, LI Wen-xiong, ZHOU Fan. Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning [J]. Computer Science, 2022, 49(7): 18-24.
[8] WANG Jie, LI Xiao-nan, LI Guan-yu. Adaptive Attention-based Knowledge Graph Completion [J]. Computer Science, 2022, 49(7): 204-211.
[9] CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241.
[10] HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279.
[11] MA Rui-xin, LI Ze-yang, CHEN Zhi-kui, ZHAO Liang. Review of Reasoning on Knowledge Graph [J]. Computer Science, 2022, 49(6A): 74-85.
[12] DENG Kai, YANG Pin, LI Yi-zhou, YANG Xing, ZENG Fan-rui, ZHANG Zhen-yu. Fast and Transmissible Domain Knowledge Graph Construction Method [J]. Computer Science, 2022, 49(6A): 100-108.
[13] DU Xiao-ming, YUAN Qing-bo, YANG Fan, YAO Yi, JIANG Xiang. Construction of Named Entity Recognition Corpus in Field of Military Command and Control Support [J]. Computer Science, 2022, 49(6A): 133-139.
[14] HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong. Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration [J]. Computer Science, 2022, 49(6): 149-157.
[15] XIONG Zhong-min, SHU Gui-wen, GUO Huai-yu. Graph Neural Network Recommendation Model Integrating User Preferences [J]. Computer Science, 2022, 49(6): 165-171.
Full text



No Suggested Reading articles found!