Computer Science ›› 2023, Vol. 50 ›› Issue (3): 129-138.doi: 10.11896/jsjkx.220300004
• Database & Big Data & Data Science • Previous Articles Next Articles
ZHOU Mingqiang, DAI Kailang, WU Quanwang, ZHU Qingsheng
CLC Number:
[1]WU J X,ZHANG Z H.Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest [J].Computer Science,2021,48(5):147-154. [2]HE Y F,ZHANG Y W,LV Z H,et al.Meta Path-Aware RatingCollaborative Filtering in Heterogeneous Information Network [J].Chinese Journal of Computers,2020,43(12):2385-2397. [3]KOREN Y.Factor in the Neighbors:Scalable and Accurate Collaborative Filtering [J].ACM Transactions on Knowledge Discovery from Data,2010,4(1):1-24 [4]XU K,ZHENG X,CAI Y,et al.Improving User Recommendation by Extracting Social Topics and Interest Topics of Users in Uni-Directional Social Networks [J].Knowledge-Based Systems,2018,140(C):120-133. [5]YANG N,MA Y,CHEN L,et al.A meta-feature based unified framework for both cold-start and warm-start explainable reco-mmendations [J].World Wide Web,2020,23(1):241-265. [6]SHI C,ZHANG Z,JI Y,et al.SemRec:A Personalized Semantic Recommendation Method Based on Weighted Heterogeneous Information Networks [J].World Wide Web,2019,22(1):153-184. [7]YIZHOU S,JIAWEI H.Mining Heterogeneous InformationNetworks:Principles and Methodologies [M].California:Morgan & Claypool,2012:57-72. [8]YAN S,WANG H,LI Y,et al.Attention-aware metapath-based network embedding for HIN based recommendation [J].Expert Systems with Applications,2021,174(8):114601.1-114601.12. [9]SHI C,HU B,ZHAO W X,et al.Heterogeneous InformationNetwork Embedding for Recommendation [J].IEEE Transactions on Knowledge & Data Engineering,2019,31(2):357-370. [10]DONG Y,CHAWLA N V,SWAMI A.metapath2vec:Scalable Representation Learning for Heterogeneous Networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Halifax,NS,Canada:Association for Computing Machinery,2017:135-144. [11]ZHAO J L,ZHAO Z Y.Recommendation Algorithm Based onHeterogeneous Information Network Embedding and Attention Neural Network [J].Computer Science,2021,48(8):72-79. [12]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems [J].Computer,2009,42(8):30-37. [13]FORSATI R,MAHDAVI M,SHAMSFARD M,et al.Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation [J].ACM Transactions on Information Systems,2014,32(4):1-38. [14]LIANG D,ALTOSAAR J,CHARLIN L,et al.FactorizationMeets the Item Embedding:Regularizing Matrix Factorization with Item Co-Occurrence[C]//Proceedings of the 10th ACM Conference on Recommender Systems.Association for Computing Machinery,2016:59-66. [15]CAO D,NIE L,HE X,et al.Embedding Factorization Models for Jointly Recommending Items and User Generated Lists[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.Association for Computing Machinery,2017:585-594. [16]LUO L,XIE H,RAO Y,et al.Personalized recommendation by matrix co-factorization with tags and time information [J].Expert Systems with Applications,2019,119(1):311-321. [17]ZHANG Z,LIU Y,ZHANG Z,et al.Fused matrix factorization with multi-tag,social and geographical influences for POI re-commendation[J].World Wide Web,2019,22(3):1135-1150. [18]NGUYEN V D,HUYNH V N,SRIBOONCHITTA S.Integrating Community Context Information Into a Reliably Weighted Collaborative Filtering System Using Soft Ratings [J].IEEE Transactions on Systems,Man,Cybernetics:Systems,2020,50(4):1318-1330. [19]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. [20]YU X,REN X,SUN Y,et al.Personalized Entity Recommendation:A Heterogeneous Information Network Approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining.Association for Computing Machinery,2014:283-292. [21]LIANG T,CHEN L,WU J,et al.Meta-Path Based Service Re-commendation in Heterogeneous Information Networks[C]//Service-Oriented Computing.Springer International Publishing,2016:371-386. [22]DAI F,GU X,LI B,et al.Meta-Graph Based Attention-Aware Recommendation over Heterogeneous Information Networks[C]//Computational Science(ICCS 2019).Springer Interna-tional Publishing,2019:580-594. [23]WU Z,PAN S,CHEN F,et al.A Comprehensive Survey onGraph Neural Networks [J].IEEE Transactions on Neural Networks and Learning Systems,2021,32(1):4-24. [24]HAMILTON W L,YING R,LESKOVEC J.Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.Long Beach,California,USA:Curran Associates Inc.,1025-1035. [25]YING R,HE R,CHEN K,et al.Graph Convolutional Neural Networks for Web-Scale Recommender Systems[C]//Procee-dings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.London,United Kingdom:Association for Computing Machinery,2018:974-983. [26]FU J,LIU J,JIANG J,et al.Scene Segmentation With Dual Relation-Aware Attention Network [J].IEEE Transactions onNeural Networks and Learning Systems,2021,32(6):2547-2560. [27]SALAKHUTDINOV R,MNIH A.Probabilistic matrix factorization [C]//Proceedings of the 21st International Conference on Neural Information Processing Systems(NIPS’08),Curran Associates Inc.2008:1257-1264. |
[1] | LI Shuai, XU Bin, HAN Yike, LIAO Tongxin. SS-GCN:Aspect-based Sentiment Analysis Model with Affective Enhancement and Syntactic Enhancement [J]. Computer Science, 2023, 50(3): 3-11. |
[2] | CHEN Fuqiang, KOU Jiamin, SU Limin, LI Ke. Multi-information Optimized Entity Alignment Model Based on Graph Neural Network [J]. Computer Science, 2023, 50(3): 34-41. |
[3] | CAO Jinjuan, QIAN Zhong, LI Peifeng. End-to-End Event Factuality Identification with Joint Model [J]. Computer Science, 2023, 50(2): 292-299. |
[4] | ZOU Yunzhu, DU Shengdong, TENG Fei, LI Tianrui. Visual Question Answering Model Based on Multi-modal Deep Feature Fusion [J]. Computer Science, 2023, 50(2): 123-129. |
[5] | QU Zhong, WANG Caiyun. Crack Detection of Concrete Pavement Based on Attention Mechanism and Lightweight DilatedConvolution [J]. Computer Science, 2023, 50(2): 231-236. |
[6] | LIU Luping, ZHOU Xin, CHEN Junjun, He Xiaohai, QING Linbo, WANG Meiling. Event Extraction Method Based on Conversational Machine Reading Comprehension Model [J]. Computer Science, 2023, 50(2): 275-284. |
[7] | LI Xuehui, ZHANG Yongjun, SHI Dianxi, XU Huachi, SHI Yanyan. AFTM:Anchor-free Object Tracking Method with Attention Features [J]. Computer Science, 2023, 50(1): 138-146. |
[8] | ZHAO Qian, ZHOU Dongming, YANG Hao, WANG Changchen. Image Deblurring Based on Residual Attention and Multi-feature Fusion [J]. Computer Science, 2023, 50(1): 147-155. |
[9] | ZHENG Cheng, MEI Liang, ZHAO Yiyan, ZHANG Suhang. Text Classification Method Based on Bidirectional Attention and Gated Graph Convolutional Networks [J]. Computer Science, 2023, 50(1): 221-228. |
[10] | CAI Xiao, CEHN Zhihua, SHENG Bin. SPT:Swin Pyramid Transformer for Object Detection of Remote Sensing [J]. Computer Science, 2023, 50(1): 105-113. |
[11] | ZHANG Jingyuan, WANG Hongxia, HE Peisong. Multitask Transformer-based Network for Image Splicing Manipulation Detection [J]. Computer Science, 2023, 50(1): 114-122. |
[12] | ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63. |
[13] | HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82. |
[14] | LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi. Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network [J]. Computer Science, 2022, 49(9): 92-100. |
[15] | DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145. |
|