Computer Science ›› 2021, Vol. 48 ›› Issue (9): 21-35.doi: 10.11896/jsjkx.201100083
Special Issue: Intelligent Data Governance Technologies and Systems
• Intelligent Data Governance Technologies and Systems • Previous Articles Next Articles
DONG Xiao-mei, WANG Rui, ZOU Xin-kai
CLC Number:
[1]LIU J L,LI X G.Techniques for Recommendation System:A Survey[J].Computer Science,2020,47(7):47-55. [2]CHANG L,CAO Y T,SUN W P,et al.Review on Tourism Recommendation System[J].Computer Science,2017,44(10):1-6. [3]WEI LF,CHEN CC,ZHANG L,et al.Security Issues and Privacy Preserving in Machine Learning[J].Journal of Computer Research and Development,2020,57(10):2066-2085. [4]CAO C P,XU B B.Research of Privacy-preserving Tag-based Recommendation Algorithm[J].Computer Science,2017,44(8):134-139. [5]BINJUBIER M,AHMED A A,ISMAIL M A B,et al.Comprehensive Survey on Big Data Privacy Protection[J].IEEE Access,2019(8):20067-20079. [6]FAN S,JIA Q,CHENG W.Safety Monitoring by A Graph-Re-gularized Semi-Supervised Nonnegative Matrix Factorization with Applications to A Vision- Based Marking Process[J].IEEE Access,2020(8):112278-112286. [7]HITAJ B,ATENIESE G,PEREZ-CRUZ F.Deep Models Under the GAN:Information Leakage from Collaborative Deep Lear-ning[C]//ACM SIGSAC Conf. Comput.New York:Commun Secur,2017:603-618. [8]CALANDRINO J A,KILZER A,NARAYANAN A,et al.You Might Also Like:Privacy Risks of Collaborative Filtering[C]//2011 IEEE Symposium on Security and Privacy (SP).Paris,IEEE,2011:231-246. [9]CAO K,GUO J,MENG G,et al.Points-of-interest Recommendation Algorithm Based on LBSN in Edge Computing Environment[J].IEEE Access,2020(8):47973-47983. [10]LOPS P,JANNACH D,MUSTO C,et al.Trends in content-based recommendation[J].User Modeling and User-Adapted Interaction,2019,29(2):239-249. [11]HUYNH H X,PHAN N Q,PHAM N M,et al.Context-Similarity Collaborative Filtering Recommendation[J].IEEE Access,2020(8):33342-33351. [12]ZHONG X L,ZHANG Y W,YAN D C,et al.Recommendations for Mobile Apps Based on the HITS Algorithm Combined with Association Rules[J].IEEE Access,2019(7):105572-105582. [13]YI M,DENG W.AUtility-Based Recommendation Approach for E-Commerce Websites Based on Bayesian Networks[C]//International Conference on Business Intelligence & Financial Engineering.Hangzhou,IEEE,2009:571-574. [14]TAO S,QIU R,PING Y,et al.Making Explainable Friend Recommendations Based on Concept Similarity Measurements via a Knowledge Graph[J].IEEE Access,2020(8):146027-146038. [15]XU C,XU L,LU Y,et al.E-government recommendation algorithm based on probabilistic semantic cluster analysis in combination of improved collaborative filtering in big-data environment of government affairs[J].Personal and Ubiquitous Computing,2019,23(3/4):475-485. [16]LIN F,ZHOU Y,YOU I,et al.Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment[J].IEEE Access,2019(7):53677-53686. [17]CHAABANE I,GUERMAZI R,HAMMAMI M.Enhancingtechniques for learning decision trees from imbalanced data[J].Advances in Data Analysis & Classification,2019(15):677-745. [18]AHMAD T,MAO H,LIN L,et al.Action Recognition usingAttention-Joints Graph Convolutional Neural Networks[J].IEEE Access,2019(8):305-313. [19]SHI S,LI Y,YANG D,et al.DOA Estimation of Coherent Signals Based on the Sparse Representation for Acoustic Vector-Sensor Arrays[J].Circuits Systems and Signal Processing,2020,39(1):3553-3573. [20]CHOI S M,JANG K,LEE T D,et al.Alleviating Item-SideCold-Start Problems in Recommender Systems Using Weak Supervision[J].IEEE Access,2020(8):167747-167756. [21]LI G Q,DUAN X X,WU C Z.A New DC Algorithm for Sparse Optimal Scoring Problem[J].IEEE Access,2020(8)53962-53971. [22]JIANG L L,CHENG Y T,LI Y,et al.A trust-based collaborative filtering algorithm for E-commerce recommendation system[J].Journal of Ambient Intelligence & Humanized Computing,2018,4(18):1-12. [23]FENG Z.Employing BP Neural Networks to Alleviate the Sparsity Issue in Collaborative Filtering Recommendation Algorithms[J].Journal of Computer Research and Development,2006,43(4):667-668. [24]YANG L.Uncertainty prediction method for traffic flow based on K-nearest neighbor algorithm[J].Journal of Intelligent and Fuzzy Systems,2020,39(22):1-11. [25]ZHANG Y,WANG Y,WANG S.Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data[J].IEEE Access,2020 (8):51324-51332. [26]QIAN Y,LI Y,WANG Y,et al.Based on Collaborative Filtering Personalized Recommendation for Online Learning[C]//2019 6th International Conference on Dependable Systems and Their Applications (DSA).2020:519-520. [27]ZHANG W,BAI Y,ZHENG J,et al. Neural Network Collaborative Filtering for Group Recommendation[M].NewYork:Springer,2018:131-143. [28]ZHANG Y,ZHANG N,SUN D,et al.An efficient Hessianbased algorithm for solving large-scale sparse group Lasso problems[J].IEEE,2017,1(179):23-63. [29]TAO Q,WU G,CHU D.Improving Sparsity and Scalability in Regularized Nonconvex Truncated-Loss Learning Problems[J].IEEE Trans. Neural. Netw. Learn. Syst.,2018,29(99):2782-2793. [30]WU L F,JIN Z,FANG Q.Improved Personalized Recommendation based on Causal Association Rule and Collaborative Filtering[J].International Journal of Distance Education Technologies,2016,14(3):21-33. [31]HASNINE M N,AKAPNAR G,FLANAGAN B,et al.Design of a Location-based Word Recommendation System Based on Association Rule Mining Analysis[C]//2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI).IEEE,2020:250-253. [32]SWAMY M K,REDDY P K,BHALLA S.Database and Expert Systems Applications[M].New York:Springer,2017:340-350. [33]ZHENG Y,PU A.Utility-Based Multi-Stakeholder Recommendations by Multi-Objective Optimization[J].IEEE/WIC/ACM International Conference on Web Intelligence (WI),2018,10(99):128-135. [34]YI M,DENG W.A Utility-Based Recommendation Approachfor E-Commerce Websites Based on Bayesian Networks[C]//International Conference on Business Intelligence & Financial Engineering.IEEE,2009:571-574. [35]ROSA R L,SCHWARTZ G M,RUGGIERO W V,et al.AKnowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning[J].IEEE Industrial Electro-nics Society,2018,15(4):2124-2135. [36]WANG X,ZHANG R,LEE Y K,et al.Web and Big Data [M].New York:Springer,2017:455-469. [37]SHU Z T U,WANG Z.A POI-Sensitive Knowledge Graph Based Service Recommendation Method[J].2019 IEEE International Conference on Services Computing (SCC),2019,10(76):197-201. [38]DARVISHY A,IBRAHIM H,SIDI F,et al.HYPNER:A Hybrid Approach for Personalised News Recommendation[J].IEEE Access,2020(8):46877-46894. [39]LI G,ZHU T,HUA J,et al.Asking Images:Hybrid Recommendation System for Tourist Spots by Hierarchical Sampling Statistics and Multimodal Visual Bayesian Personalized Ranking[J].IEEE Access,2019(7):126539-126560. [40]ZOU L,CHEN G,LI J.Time weighted hybrid recommendation algorithm[J].Computer Science,2016,43(S2):451-454. [41]YANG C,REN S,LIU Y,et al.Personalized Channel Recom-mendation Deep Learning From a Switch Sequence[J].IEEE Access,2018,6:50824-50838. [42]CHEN Z,ZHU S,NIU Q,et al.Knowledge Discovery and Re-commendation With Linear Mixed Model[J].IEEE Access,2020(8):38304-38317. [43]HU X,LIU Q,LI L,et al.Database Systems for Advanced Applications[M].New York:Springer,2018:74-86. [44]ZHOU S,LV S,ZENG C,et al.Advances on P2P,Parallel,Grid,Cloud and Internet Computing[M].New York:Springer,2019:494-502. [45]DNG J,LI X,XU C,et al.Feature Re-Learning with Data Augmentation for Content-based Video Recommendation[C]//ACM Multimedia.ACM,2018. [46]IMMANENI N,PADMANABAN I,RAMASUBRAMANIANB,et al.A meta-level hybridization approach to personalized movie recommendation[C]//2017 International Conference on Advances in Computing,Communications and Informatics (ICACCI).2017:2193-2200. [47]YASI R,SALEE M,SHAIK H.Privacy preserving internet of things recommender systems for smart cities[M].New York:Springer,2020. [48]HITAJ B,ATENIESE G,PEREZ-CRUZ F.Deep models under the GAN:Information leakage from collaborative deep learning[C]//ACM SIGSAC Conf. Comput.New York,2017:603-618. [49]DWORK C,MCSHERRY F,NISSIM K,et al.Calibrating Noise to Sensitivity in Private Data Analysis[C]//Proceedings of the Third conference on Theory of Cryptography.New York:Springer-Verlag,2006:265-284. [50]LI F H,LI H,JIA Y,et al.Research scope and development trend of privacy computing[J].Acta communication Sinica,2016,37(4):1-11. [51]ZHANG T,ZHU T,XIONG P,et al.Correlated Differential Privacy:Feature Selection in Machine Learning[J].IEEE Transactions on Industrial Informatics,2020,16(3):2115-2124. [52]CHENG X,TANG P,SU S,et al.Multi-Party High-DimensionalData Publishing under Differential Privacy[J].IEEE Transactions on Knowledge and Data Engineering,2020,32(8):1557-1571. [53]MCSHERRY F.Privacy Integrated Queries:An ExtensiblePlatform for Privacy-Preserving Data Analysis[J].Communications of the Acm,2010,53(9):89-97. [54]KIFER D,LIN B R.Towards an axiomatization of statistical privacy and utility[C]//Twenty-ninth Acm Sigmod-sigact-sigart Symposium on Principles of Database Systems.ACM,2010:147-158. [55]GAI K,WU Y,ZHU L,et al.Differential Privacy-Based Blockchain for Industrial Internet-of-Things[J].IEEE Transactions on Industrial Informatics,2020,16(6):4156-4165. [56]LANTZ,ERIC,BOYD K,et al.Subsampled Exponential Mechanism:Differential Privacy in Large Output Spaces[J].ACM Workshop on Artificial I ntelligence and Security ACM,2015,27(56):25-33. [57]PHAN N,WANG Y,WU X,et al.Personal Analytics and Privacy[M].NewYork:Springer,2017:23-35. [58]ZHOU J,DONG X L,CAO Z F.Research Advances on Privacy Preserving in Recommender Systems[J].Journal of Computer Research and Development,2019,56(10):2033-2048. [59]XIAN Z Z,LI Q L,HUANG X Y,et al.Collaborative filtering algorithm based on differential privacy and SVD++[J].Control and Decision,2019,34(1):43-54. [60]LI M,ZENG Y,GUO Y,et al.Security and Privacy in SocialNetworks and Big Data[M].New York:Springer,2020:318-328. [61]CALANDRINO J A,KILZER A,NARAYANAN A,et al.You Might Also Like:Privacy Risks of Collaborative Filtering[C]//2011 IEEE Symposium on Security and Privacy (SP).New York:IEEE,2011:231-246. [62]BERLIOZ A,FRIEDMAN A,KAAFAR M,et al.Applying differential privacy to matrix factorization[C]//Procofthe 9th ACM Conf on Recommender Systems.New York:ACM,2015:107-114. [63]STEINER T A.Privacy and Identity Management.Data for Better Living:AI and Privacy[M].New York:Springer,2020:395-410. [64]WANG J,TANG Q.Differentially Private Neighborhood-Based Recommender Systems[C]//IFIP International Conference on ICT Systems Security and Privacy Protection.New York:Springer,2017:459-473. [65]LI T,SONG L,FRAGOULI C.Federated Recommendation System via Differential Privacy[J].IEEE,2020,56(26):2592-2597. [66]ZHOU H,YANG G,XU Y,et al.Science of Cyber Security [M].New York:Springer,2019:235-249. [67]YIN C,SHI L,WANG J Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy Protection[C/OL].International conference on future information technology.https://www.zhangqiaokeyan.com/academic-conference-foreign_internatial-cference-future-informati-t_thesis/020512470422.html. [68]SHIN H,KIM S,SHIN J,et al.Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy[J].IEEE Transactions on Knowledge & Data Engineering,2018,30(9):1770-1782. [69]HE M,CHANG M M,WU X F.A Collaborative Filtering Recommendation Method Based on Differential Privacy[J].Journal of Computer Research and Development,2017,54(7):1439-1451. [70]YU F,WAN G,MI N,et al.DNN-DP:Differential Privacy Enabled Deep Neural Network Learning Framework for Sensitive Crowdsourcing Data[J].IEEE Transactions on Computational Social Systems,2019,7(1):215-224. [71]FENG P,ZHU H,LIU Y,et al.Differential Privacy Protection Recommendation Algorithm Based on Student Learning Beha-vior[C]//2018 IEEE 15th International Conference on e-Business Engineering (ICEBE).IEEE,2018. [72]PENG H L,ZHANG X J,JIN K Z.Social RecommendationsMethod Based on Differential Privacy[J].Computer Science,2017,44(Z6):395-398. [73]FRIEDMAN A,BERKOVSKY S,KAAFAR M A.A differential privacy framework for matrix factorization recommender systems[J].User Modeling and User-Adapted Interaction,2016,26(5):1-34. [74]ZHAO J,CHEN Y,ZHANG W.Differential Privacy Preservation in Deep Learning:Challenges,Opportunities and Solutions[J].IEEE Access,2019(7):48901-48911. [75]CHENG H P,YU P,HU H J,et al.Cloud Computing-CLOUD[M].New York:Springer,2019:130-145. [76]XIAN Z,LI Q,HUANG X,et al.New SVD-based collaborative filtering algorithms with differential privacy[J].Journal of Intelligent and Fuzzy Systems,2017,33(4):2133-2144. [77]SHIN H,KIM S,SHIN J,et al.Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy[J].IEEE Transactions on Knowledge & Data Engineering,2018,30(9):1770-1782. [78]HE M,CHANG M M,WU X F.A Collaborative Filtering Re-commendation Method Based on Differential Privacy[J].Journal of Computer Research and Development,2017,54(7):1439-1451. [79]LYU D,CHEN L,XU Z,et al.Weighted multi-information constrained matrix factorization for personalized travel location re-commendation based on geo-tagged photos[J].Applied Intelligence,2020,50(1):1-15. [80]YUN J,ZHANG C,LING X,et al.A Multi-Trans Matrix Factorization Model With Improved Time Weight in Temporal Re-commender Systems[J].IEEE Access,2019,16(99):2408-2416. [81]AMEEN T,CHEN L,XU Z,et al.A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos[J].International Journal of Geo-Information,2020,9(8):464. [82]SIFA R,YAWAR R,RAMAMURTHY R,et al.Matrix- and Tensor Factorization for Game Content Recommendation[J].KI-Künstliche Intelligenz,2019,34(4):57-67. [83]FIORESI R,CHAUDHARI P,SOATTO S.A geometric interpretation of stochastic gradient descent using diffusion metrics[J].IEEE Access,2020,22(1):101. [84]ZHU W,HUANG K,XIAO X,et al.ALSBMF:predicting ln-cRNA-disease associations by alternating least squares based on matrix factorization[J].IEEE Access,2020(8):26190-26198. [85]MECKLENBRAUKER C F,GERSTOFT P.Maximum-likeli-hood DOA estimation at low SNR in Laplace-like noise[C]//2019 27th European Signal Processing Conference (EUSIPCO).Spain:EUSIPCO,2019:1-5. [86]ZHU T Q,LI G,ZHOU W L,et al.Privacy-preserving topic model for tagging recommender systems[J].Knowledge & Information Systems,2016,46(1):33-58. [87]HUA J Y,XIA C,ZHONG S.Differentially private matrix factorization[C]//Proc of the 24th International Conference on Artificial Intelligence.Austin:AAAI Press.2015:1763-1770. [88]CAO C P,XU B B.Research of privacy-preserving tag-basedrecommendation algorithm[J].Computer Science,2017,44(8):134-139. [89]ZHENG J,WANG X Q.Differential privacy matrix factorization recommendation algorithm fused with tag similarity[J].Application Research of Computers,2020,37(3):851-855. [90]FRIEDMAN A,BERKOVSKY S,KAAFAR M A.A differential privacy framework for matrix factorization recommender systems[J].User Modeling and User-Adapted Interaction,2016,26(5):1-34. [91]NASSAR N,JAFAR A,RAHHAL Y.Correction to:Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization[J/OL].Journal of Big Data,2020.https://www.researchgate.net/publication/341610042_Multi-criteria_collaborative_filtering_recommender_by_fusing_deep_neural_network_and_matrix_factorization. [92]GOLDBERG D,NICHOLS D,OKI B M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of the Acm,1992,35(12):61-70. [93]JI D,XIANG Z,LI Y.Dual Relations Network for Collaborative Filtering[J].IEEE Access,2020(8):109747-109757. [94]MIRZAEI G,MANSOURI N,JAMALI M M.Parallel Bayesian Belief Network in Building Energy Conservation[C]//2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS).IEEE,2019:468-471. [95]LI Z,LI Y,LU W,et al.Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm[J].IEEE Access,2020(8):92615-92626. [96]LI W,SHI Q,SIBTAIN M,et al.A Hybrid Forecasting Model for Short-Term Power Load Based on Sample Entropy,Two-Phase Decomposition and Whale Algorithm Optimized Support Vector Regression[J].IEEE Access,2020,12(20):7-21. [97]KOREN Y,BELL R,VOLINSKY C.Matrix FactorizationTechniques for Recommender Systems[J].Computer,2009,42(8):30-37. [98]KOREN Y,BELL R,VOLINSKY C.Matrix FactorizationTechniques for Recommender Systems[J].Computer,2009,42(8):30-37. [99] YIN C,SHI L,WANG J.Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy Protection[C/OL].International conference on future information technology.https://www.zhangqiaokeyan.com/academic-conference-foreign_internatial-cference-future-informati-t_thesis/020512470422.html. [100]ZHU X,SUN Y.Differential Privacy for Collaborative Filtering Recommender Algorithm[C]//Acm on International Workshop on Security & Privacy Analytics.ACM,2016:9-16. [101]REN J,XU X,YU H.Improved Collaborative Filtering Algorithm Incorporating User Information and Using Differential Privacy[C]//CCF Conference on Computer Supported Cooperative Work, and Social Computing.Springer,Singapore,2019. [102]WU W M,HE J,LU L B,et al.Design and implementation of collaborative filtering recommendation system based on differential privacy[J].Information and Computer (Theoretical Edition),2018(17):68-70. [103]JIANG Z L,QIAO X M.Fuzzy C-means clustering recommendation based on differential privacy protection[J].Computer Systems & Applications,2018,27(10):193-199. [104]WANG H H,WU X,YU X,et al.Research on Differential Privacy Protection of TopN Recommendation System[J].Chinese Science and Technology Paper,2017,12(20):49-53. [105]ABAS A R,ELHENAWY I,MOHAMED H,et al.Deep Lear-ning Model for Fine-Grained Aspect-Based Opinion Mining[J].IEEE Access,2020(8):128845-128855. [106]WANG J S,ZHANG G M,HU B.A review of deep learning based recommendation algorithm[J].Journal of Nanjing Normal University:Engineering Technology Edition,2018,18(4):39-49. [107]OORD A V D,DIELEMAN S,SCHRAUWEN B.Deep content.based music recommendation[C]//Conference on Neural Information Processing Systems(NIPS).Lake Tahoe:NIPS,2013:1-1. [108]ZHOU J,ALBATAL R,GURRIN C.Applying Visual User Interest Profiles for Recommendation and Personalisation[J].2016,83(16):56-78. [109]BANSAL T,BELANGER D,MCCALLUM A.Ask the GRU:Multi-Task Learning for Deep Text Recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems September.New York:RecSys ’16,2016:107-114. [110]COVINGTON P,ADAMS J,SARGIN E.Deep Neural Net-works for YouTube Recommendations[C]//Acm Conference on Recommender Systems.New York:ACM,2016:191-198. [111]BHARADHWAJ H,PARK H,LIM B Y.RecGAN:recurrent generative adversarial networks for recommendation systems[C]//12th ACM Conference.New York:ACM,2018. [112]WANG Q,YIN H,HU Z,et al.Neural Memory StreamingRe-commender Networks with Adversarial Training[C]//Procee-dings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.July.London:ACN,2018:2467-2475. [113]ZHAO W,WANG B,YE J,et al.PLASTIC:Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training[C]//Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18.Vienna:IJCAI,2018. [114]CHEN X,LI S,LI H,et al.Generative Adversarial User Model for Reinforcement Learning Based Recommendation System[C]//Proceedings of the 36th International Conference on Machine Learning.Long Beach:PMLR,2019:1052-1061. [115]LIU W,WANG Z J,YAO B,et al.Geo-ALM:POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism[C]//28th IJCAI.Macao:IJCAI,2019:1807-1813. [116]ZHOU F,YIN R,ZHANG K,et al.Adversarial Point-of-In-terest Recommendation[C]//The World Wide Web Conference.Franscisco:ACM,2019:2022-2032. [117]LIN H N,TSUKASA I.Domain-to-Domain Translation Modelfor Recommender System[J/OL].Computer Science.https://arxiv.org/abs/1812.06229. [118]WANG C,NIEPERT M,LI H.RecSys-DAN:DiscriminativeAdversarial Networks for Cross-Domain Recommender Systems[J].IEEE Transactions on Neural Networks & Learning Systems,2019,45(24):1-10. [119]PERERA D,ZIMMERMANN R.CnGAN:Generative Adver-sarial Networks for Cross-network User Preference Generation for Non-overlapped Users[C]//The Web Conference 2019.New York:ACM,2019:1-1. [120]MARTÍ,ABADI N,CHU A,et al.Deep Learning with Differential Privacy[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (ACM CCS).New York,2016:308-318. [121]XU C,REN J,ZHANG D,et al.GANobfuscator:Mitigating Information Leakage Under GAN via Differential Privacy[J].IEEETransactions on Information Forensics and Security,2019,14(9):2358-2371. [122]YUAN D,ZHU X,WEI M,et al.Collaborative Deep Learningfor Medical Image Analysis with Differential Privacy[C]//2019 IEEE Global Communications Conference (GLOBECOM).Waikoloa,HI,USA,2019:1-6. [123]FRIEDMAN A,BERKOVSKY S,KAAFAR M A.A differential privacy framework for matrix factorization recommender systems[J].User Modeling and User-Adapted Interaction,2016,26(5):1-34. [124]FENG P,ZHU H,LIU Y,et al.Differential Privacy Protection Recommendation Algorithm Based on Student Learning Beha-vior[C]//2018 IEEE 15th International Conference on e-Business Engineering (ICEBE).New York:IEEE,2018. [125]ANDRIY M,RUSLAN R.Probabilistic matrix factorization[C]//Conference and Workshop on Neural Information Proces-sing Systems.Canada:NIPS,2008:1257-1264. [126]GOODFELLOWI J,POUGET-ABADIE J,M M,et al.Generative adversarial nets[C]//Conference and Workshop on Neural Information Processing Systems.Canada:NIPS,2014:2672-2680. [127]ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein ge-nerative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning.New York:PMLR,2017:214-223. [128]ZHANG X,JI S,WANGT.Differentially private releasing via deep generative model[J/OL].Computer Science.https://arxiv.org/abs/1801.01594 [129]XIE L,LIN K,WANG S,et al.Differentially private generative adversarial network[J/OL].Computer Science.https://arxiv.org/abs/1802.06739v1. |
[1] | CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13. |
[2] | WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54. |
[3] | 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. |
[4] | 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. |
[5] | TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305. |
[6] | WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293. |
[7] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[8] | JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335. |
[9] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
[10] | HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163. |
[11] | ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169. |
[12] | SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235. |
[13] | HUANG Jue, ZHOU Chun-lai. Frequency Feature Extraction Based on Localized Differential Privacy [J]. Computer Science, 2022, 49(7): 350-356. |
[14] | SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56. |
[15] | HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78. |
|