Computer Science ›› 2021, Vol. 48 ›› Issue (4): 104-110.doi: 10.11896/jsjkx.200800027
• Database & Big Data & Data Science • Previous Articles Next Articles
LIU Zhi-xin, ZHANG Ze-hua, ZHANG Jie
CLC Number:
[1]SHI C,HU B,ZHAO W X,et al.Heterogeneous information network embedding for recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2018,31(2):357-370. [2]ZHU J,ZHANG J,ZHANG C,et al.CHRS:Cold startrecommendation across multiple heterogeneous information networks[J].IEEE Access,2017,5:15283-15299. [3]WANG X,HOI S C H,ESTER M,et al.Learning personalized preference of strong and weak ties for social recommendation[C]//Proceedings of the 26th International Conference on World Wide Web.2017:1601-1610. [4]ZHANG J,LI T,JIANG Z,et al.A Novel Weighted Meta Graph Method for Classification in Heterogeneous Information Networks[J].Applied Sciences,2020,10(5):1603. [5]CHEN Y,WANG C.HINE:Heterogeneous information net-work embedding[C]//Proceedings of the International Conference on Database Systems for Advanced Applications.Springer,Cham,2017:180-195. [6]ZHAO H,YAO Q,LI J,et al.Meta-graph based recommendation fusion over heterogeneous informationnetworks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:635-644. [7]SHI C,ZHANG Z,LUO P,et al.Semantic path based personalized recommendation on weighted heterogeneous information networks[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.2015:453-462. [8]HU B,SHI C,ZHAO W X,et al.Leveraging meta-path basedcontext for top-n recommendation with a neural co-attention model[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2018:1531-1540. [9]苗夺谦,王国胤,刘清,等.粒计算:过去,现在与展望[M].北京:科学出版社,2007. [10]ZADEHL 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. [11]HU Q H,YU D R,XIE Z X.Numerical attribute reductionbased on neighborhood granulation and rough approximation[J].Journal of Software,2008,19(3):640-649. [12]QIAN Y,LIANG X,WANG Q,et al.Local rough set:a solution to rough data analysis in big data[J].International Journal of Approximate Reasoning,2018,97:38-63. [13]ZHAO X,ZHANG Z H,ZHANG C W,et al.RGNE:A Net-work Embedding Method for Overlapping Community Detection Based on Rough Granulation[J].Journal of Computer Research and Development,2020,57(6):1302-1311. [14]SHI C,LIU J,ZHUANG F,et al.Integrating heterogeneous information via flexible regularization framework for recommendation[J].Knowledge and Information Systems,2016,49(3):835-859. [15]DAI F,GU X,LI B,et al.Meta-Graph Based Attention-Aware Recommendation over Heterogeneous Information Networks[C]//Proceedings of the International Conference on Computational Science.Springer,Cham,2019:580-594. [16]ZHANG Z W,CUI P,ZHU W W.Deep learning on graphs:A survey[J].arXiv:1812.04202v3,2020. [17]BERG R,KIPF T N,WELLING M.Graph convolutional matrixcompletion[C]//Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2018. [18]ZHENG L,LU C T,JIANG F,et al.Spectral collaborative filtering[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:311-319. [19]PENG H,LI J,GONG Q,et al.Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.IJCAI,2019:3238-3245. [20]WANG X,JI H,SHI C,et al.Heterogeneous Graph Attention Network[C]//Proceedings of the World Wide Web Conference.ACM,2019:2022-2032. [21]FAN S,ZHU J,HAN X,et al.Metapath-guided heterogeneous graph neural network for intent recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2478-2486. [22]SUN Y,HAN J,YAN X,et al.Pathsim:Meta path-based top-k similarity search in heterogeneous information networks[J].Proceedings of the VLDB Endowment,2011,4(11):992-1003. [23]VELIKOVI P,CUCURULL G,CASANOVA A,et al.Graph attention networks[C]//Proceedings of the 6th International Conference on Learning Representations.ICLR,2018. [24]HE X,LIAO L,ZHANG H,et al.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web.2017:173-182. [25]KINGMA D P,BA J.Adam:a method for stochastic optimization[C]//Proceedings of the 3rd International Conference on Learning Representations.ICLR,2015. [26]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.Bpr:Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence.UAI,2009:452-461. |
[1] | SHI Dian-xi, ZHAO Chen-ran, ZHANG Yao-wen, YANG Shao-wu, ZHANG Yong-jun. Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning [J]. Computer Science, 2022, 49(8): 247-256. |
[2] | QIN Qi-qi, ZHANG Yue-qin, WANG Run-ze, ZHANG Ze-hua. Hierarchical Granulation Recommendation Method Based on Knowledge Graph [J]. Computer Science, 2022, 49(8): 64-69. |
[3] | TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216. |
[4] | ZENG Wei-liang, CHEN Yi-hao, YAO Ruo-yu, LIAO Rui-xiang, SUN Wei-jun. Application of Spatial-Temporal Graph Attention Networks in Trajectory Prediction for Vehicles at Intersections [J]. Computer Science, 2021, 48(6A): 334-341. |
[5] | DU Shao-hua, WAN Huai-yu, WU Zhi-hao, LIN You-fang. Customs Commodity HS Code Classification Integrating Text Sequence and Graph Information [J]. Computer Science, 2021, 48(4): 97-103. |
[6] | ZHANG Liang-cheng, WANG Yun-feng. Dynamic Adaptive Multi-radar Tracks Weighted Fusion Method [J]. Computer Science, 2020, 47(11A): 321-326. |
|