融入深度自编码器与网络表示学习的社交网络信息推荐模型

doi:10.11896/jsjkx.200400120

Abstract

Abstract: In recent years,using deep learning technology and user-trusted information to improve the recommendation system has become one of the hot topics in the academia,but it is still one of the important challenges to build a model for the recommendation system which combines the two.This paper proposes a hybrid model that expands the deep self-decoder and Top-k semantic social network information by constructing a joint optimization function.The model would collect implicit semantic information based on the network representation learning method and perform experiments with multiple real social network datasets toeva-luate the performance of the AE-NRL model (Autoencoder-Network Representation Learning Model) by various methods.The results show that the model proposed in this paper has better performance than the matrix decomposition method in more sparse and larger data sets.Compared with explicit trust links,the implicit and reliable social network information can better identify the trust degree between users.In the network representation learning technology,deep learning models (SDNE and DNGR) are more effective in the AE-NRL model.

Key words: Autoencoder, Information recommendation, Network represents learning, Social networks, User trust information

CLC Number:

TP181

GU Qiu-yang, JU Chun-hua, WU Gong-xing. Social Network Information Recommendation Model Combining Deep Autoencoder and Network Representation Learning[J].Computer Science, 2020, 47(11): 101-112.

References

[1] HUANG L W,JIANG B T,LV S Y,et al.Survey on Dep Learning Based Recommender Systems [J].Chinese Journal of Computers,2018,41(7):1619-1647.
[2] KHUSRO S,ALI Z,ULLAH I.Recommender Systems:Issues,Challenges,and Research Opportunities [M]//Information Science and Applications (ICISA).Singapore:Springer Singapore,2016:1179-1189.
[3] CRESPO R G,MARTíNEZ O S,LOVELLE J M C,et al.Recommendation System based on user interaction data applied to intelligent electronic books [J].Comput Human Behav,2011,27(4):1445-1449.
[4] DOSOVITSKIY A,SPRINGENBERG J T,RIEDMILLER M,et al.Discriminative unsupervised feature learning with convolutional neural networks[C]//Adv Neural Inf Process Syst.2014:766-774.
[5] KOREN Y,BELL R,VOLINSKY C.Matrix FactorizationTechniques for Recommender Systems [J].Comp,2009,42(8):30-37.
[6] GOYAL P,FERRARA E.Graph embedding techniques,applications,and performance:A survey [J].Knowledge-Based Systems,2018,151:78-94.
[7] CHANEY A J,BLEI D M,ELIASSI-RAD T.A probabilisticmodel for using social networks in personalized item recommendation[C]//Proceedings of the 9th ACM Conference on Recommender Systems.New York,NY,USA:Association for Computing Machinery,2015:43-50.
[8] ZHU J,MING Q.POI recommendation by incorporating trust-distrust relationship in LBSN [J].Journal on Communications,2018,39(7):157-165.
[9] TANG J,QU M,WANG M,et al.Line:Large-scale Information Network Embedding[C]//Proceedings of the 24th International Conference on World Wide Web.Florence,Italy:International World Wide Web Conferences Steering Committee,2015:1067-1077.
[10] XING S,LIU F,ZHAO X,et al.Points-of-interest recommendation based on convolution matrix factorization [J].Appl Intell,2018,48(8):2458-2469.
[11] CUI C,WANG H,LI Q.Research on collaborative filtering recommendation algorithm based on user tags [J].Journal of Systems Science and Mathematical Sciences,2019,39(3):437-448.
[12] TU C,YANG C,LIU Z,et al.Network representation learning:an overview [J].Scientia Sinica Informations,2017,47(8):980-996.
[13] JIN Z,LIU R,LI Q,et al.Predicting user's multi-interests with network embedding in health-related topics[C]//2016 International joint conference on neural networks (IJCNN).Vancouver,BC,Canada:IEEE,2016:2568-2575.
[14] GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco,California,USA:Association for Computing Machinery,2016:855-864.
[15] COATES A,NG A,LEE H.An analysis of single-layer net-works in unsupervised feature learning[C]//Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics.Proceedings of Machine Learning Research:PMLR,2011:215-223.
[16] GUO X,LU J.Intelligent e-government services with personalized recommendation techniques [J].International Journal of Intelligent Systems,2007,22(5):401-417.
[17] BENKADDOUR F Z,TAGHEZOUT N,KADDOUR-AHMEDF Z,et al.An adapted approach for user profiling in a recommendation system:Application to industrial diagnosis [J].IJIMAI,2018,5(3):118-130.
[18] XIAO Y,LIU Y,LIU H,et al.A recommendation model based on dynamic role identification and tensor decompositio [J].Acta Electronica Sinica,2018,46(3):569-574.
[19] CAO S,LU W,XU Q.Deep neural networks for learning graph representations[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.Phoenix,Arizona:AAAI Press,2016:1145-1152.
[20] DOMMETI R.Neighborhood based methods for collaborativefiltering [J].A Case Study,2009,11(1):1-5.
[21] GIZAW T Z,DONG J,OAD A.Solving cold-start problem by combining personality traits and demographic attributes in a user based recommender system [J].International Journal of Advanced Research in Computer Science and Software Engineering,2017,7(5):14-20.
[22] LANGSETH H.Bayesian networks for collaborative filtering[C]//Proceedings of Norwegian artificial intelligens symposium.2009.
[23] ZHANG C X,YU L,WANG Y,et al.Collaborative User Network Embedding for Social Recommender Systems[C]//Proceedings of the 2017 SIAM International Conference on Data Mining.2017:381-389.
[24] CHEN H,PEROZZI B,HU Y,et al.Harp:Hierarchical representation learning for networks[C]//Thirty-Second AAAI Conference on Artificial Intelligence.2018:2127-2134.
[25] DAI L,MENG X,ZHANG Y,et al.Restaurant recommendation model with multiple information fusion [J].Journal of Software,2019,30(9):2869-2885.
[26] KOUEMOU G L,DYMARSKI D P.History and theoreticalbasics of hidden Markov models [J].Hidden Markov Models,Theory and Applications,2011,10:15205.
[27] XUE S,ABDEL-HAMID O,JIANG H,et al.Direct adaptation of hybrid DNN/HMM model for fast speaker adaptation in LVCSR based on speaker code[C]//2014 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).Florence,Italy:IEEE,2014:6339-6343.
[28] HAMILTON W L,YING R,LESKOVEC J.Representationlearning on graphs:Methods and applications [J].arXiv,2017,1709:5584.
[29] ZHANG S,YAO L,SUN A,et al.Deep learning based recommender system:A survey and new perspectives [J].ACM Comput Surv,2019,52(1):1-38.
[30] SUN K,GONÇALVES J P,LARMINIE C,et al.Predicting disease associations via biological network analysis[J].BMC Bioinformatics,2014,15(1):1-13.
[31] OU M,CUI P,PEI J,et al.Asymmetric transitivity preserving graph embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco,California,USA:Association for Computing Machinery,2016:1105-1114.
[32] PENG H,JIN Y,LV X,et al.Context-aware POI recommendation based on matrix factorization [J].Chinese Journal of Computers,2019,(8):1797-1811.
[33] GUAN X,LI C-T,GUAN Y.Enhanced SVD for Collaborative Filtering[C]//Advances in Knowledge Discovery and Data Mining.Cham:Springer International Publishing,2016:503-514.
[34] YANG C,LIU T,LIU L,et al.A novel recommendation approach of electronic literature resources combining semantic and social features [J].Journal of the China Society for Scientific and Technical Information,2019,38(6):632-640.
[35] EIRINAKI M,GAO J,VARLAMIS I,et al.Recommender systems for large-scale social networks:A review of challenges and solutions [J].Future Generation Computer Systems,2018,78:413-418.
[36] HU C,TONG X,LIANG W.The real-value restricted Boltzmann machine recommendation algorithm based on trust-distrust relationship [J].Systems Engineering -Theory & Practice,2019,39(7):1817-1830.
[37] YANG B,LEI Y,LIU J,et al.Social collaborative filtering by trust [J].ITPAM,2016,39(8):1633-1647.
[38] GRBOVIC M,RADOSAVLJEVIC V,DJURIC N,et al.E-commerce in Your Inbox:Product Recommendations at Scale[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Sydney,NSW,Australia:Association for Computing Machinery,2015:1809-1818.
[39] XIAO Y,SUN H,DAI T,et al.A rating prediction methodbased on cloud model in social recommendation system [J].ActaElectronica Sinica,2018,46(7):1762-1767.
[40] HUANG L,JIANG B,LV S,et al.Survey on deep learningbased recommender systems [J].Chinese Journal of Compu-ters,2018,41(7):1619-1647.
[41] SHARMA S,MAHAJAN A.Suggestive approaches to create a recommender system for GitHub [J].Int J Inf Technol Comput Sci,2017,9(8):48-55.
[42] LI Z,QIN K,WANG Y.Approximation of finite rank quasilinear projector in banach space [J].Mathematics in Practice and Theory,2006,36(6):268-270.
[43] HUANG Y,SHANG Z,ZHANG M.Image annotation combining feedback log and mixture probabilistic model [J].Compu-ter Engineering,2012,38(21):202-205.
[44] SALAKHUTDINOV R,MNIH A.Bayesian probabilistic matrix factorization using Markov chain Monte Carlo[C]//Proceedings of the 25th international conference on Machine learning.Helsinki,Finland:Association for Computing Machinery,2008:880-887.
[45] MOCHÓN M C.Social network analysis and big data tools applied to the systemic risk supervision [J].Ijimai,2016,3(6):34-37.
[46] LI C,WU Y,QIAN Z,et al.DDoS attack detection and defense based on hybrid deep learning model in SDN [J].Journal on Communications,2018,39(7):176-187.
[47] PEROZZI B,AL-RFOU R,SKIENA S.DeepWalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.New York,New York,USA:Association for Computing Machinery,2014:701-710.
[48] SUN Y,WANG X,TANG X.Hybrid deep learning for face verification[C]//2013 IEEE International Conference on Computer Vision.Sydney,NSW,Australia:IEEE,2013:1489-1496.
[49] SAINATH T N,KINGSBURY B,RAMABHADRAN B.Auto-encoder bottleneck features using deep belief networks[C]//2012 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).Kyoto,Japan:IEEE,2012:4153-4156.
[50] PENG J,WANG T,CHEN Y,et al.User recommendation based on cross-platform online social networks [J].Journal on Communications,2018,39(3):147-158.
[51] YANG C,FAN Q,WANG T,et al.RepoLike:personal repositories recommendation in social coding communities[C]//Proceedings of the 8th Asia-Pacific Symposium on Internetware.Beijing,China:Association for Computing Machinery,2016:54-62.
[52] HE C,TANG Y,ZHANG Q,et al.Short text online clustering based on incremental robust nonnegative matrix factorization[J].Acta Electronica Sinica,2019,47(5):1086-1093.
[53] SRIVASTAVA N,SALAKHUTDINOV R R.Multimodallearning with deep boltzmann machines[C]//Adv Neural Inf Process Syst.Lake Tahoe,Nevada,USA,2012:2222-2230.
[54] QI J,LIANG X,LI Z,et al.Representation learning of large-scale complex information network:Concepts,methods and challenges [J].Chinese Journal of Computers,2018,41(10):2394-2420.
[55] HUANG Z,ZHANG B,FANG Q,et al.An efficient algorithm of information recommendation between groups in social networks [J].Acta Electronica Sinica,2015,43(6):1090-1093.
[56] SUN Z,HAN L,HUANG W,et al.Recommender systemsbased on social networks [J].Journal of Systems and Software,2015,99:109-119.
[57] LUO X,ZHOU M,LI S,et al.A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method [J].IEEE Transactions on Neural Networks and Learning Systems,2016,27(3):579-592.
[58] LUO X,LIU Z,LI S,et al.A fast non-negative latent factormodel based on generalized momentum method [J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2018,13(11):1-11.
[59] LUO X,ZHOU M,LI S,et al.Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications [J].IEEE Transactions on Cybernetics,2020,50(5):1844-1855.

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