Computer Science ›› 2020, Vol. 47 ›› Issue (11): 101-112.doi: 10.11896/jsjkx.200400120

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

Social Network Information Recommendation Model Combining Deep Autoencoder and Network Representation Learning

GU Qiu-yang1,2, JU Chun-hua3, WU Gong-xing3   

  1. 1 School of Management,Zhejiang University of Technology,Hangzhou 310023,China
    2 China Institute for Small and Medium Enterprises,Zhejiang University of Technology,Hangzhou 310023,China
    3 School of Management Science & Engineering,Zhejiang Gongshang University,Hangzhou 310018,China
  • Received:2020-04-24 Revised:2020-07-27 Online:2020-11-15 Published:2020-11-05
  • About author:GU Qiu-yang ,born in 1995,Ph.D candidate.His research interests include intelligent information processing,high-quality development of small and medium-sized enterprises,etc.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (71571162) and Zhejiang Province Social Science Planning Key Subject (20NDJC10Z).

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
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