计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 101-112.doi: 10.11896/jsjkx.200400120

• 数据库&大数据&数据科学 • 上一篇    下一篇

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

顾秋阳1,2, 琚春华3, 吴功兴3   

  1. 1 浙江工业大学管理学院 杭州 310023
    2 浙江工业大学中国中小企业研究院 杭州 310023
    3 浙江工商大学管理工程与电子商务学院 杭州 310018
  • 收稿日期:2020-04-24 修回日期:2020-07-27 出版日期:2020-11-15 发布日期:2020-11-05
  • 通讯作者: 顾秋阳(guqiuyang123@163.com)
  • 基金资助:
    国家自然科学基金项目(71571162);浙江省社科规划重点课题(20NDJC10Z)

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).

摘要: 近年来,使用深度学习技术与用户信任信息进行推荐的系统已成为学术界的研究热点之一,但要为推荐系统建立结合了这两者的模型仍是目前学界面临的重要挑战之一。文中提出了一种通过构建联合优化函数来扩展深度自解码器和Top-k语义社交网络信息的混合模型。基于网络表示学习法进行隐性语义信息采集,并使用多个真实社交网络数据集进行实验,通过多种方法评估所述AE-NRL模型(Autoencoder-Network Representation Learning Model)的性能。实验结果表明,所提模型在更稀疏且体量更大的数据集中比矩阵分解法具有更优的性能;相比显性信任链接,隐性且可靠的社交网络信息可更好地识别用户间的信任关系;在网络表示学习技术中,基于深度学习的模型(SDNE和DNGR)在AE-NRL模型中的效果更好。

关键词: 自编码器, 网络表示学习, 社交网络, 信息推荐, 用户信任信息

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, Network represents learning, Social networks, Information recommendation, User trust information

中图分类号: 

  • TP181
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