异构社交网络用户兴趣挖掘方法

doi:10.19665/j.issn1001-2400.2019.02.014

摘要/Abstract

摘要：

由于当前各类主流网络平台的发展呈现出“社交平台内容化、内容平台社交化”的趋势,用户分化也日趋明显,出现了拥有大规模粉丝的超级节点,因此,提出了一种基于社交关系的兴趣挖掘模型。结合矩阵分解和标签传播算法,将用户分为内容发布者和普通用户两类,并分别提取和计算兴趣话题,实现了在大规模异构网络中发现、挖掘用户兴趣。基于知乎数据集上设计的对比实验,验证了模型的有效性以及算法的性能优势。与基线方法相比,这种算法在查全率上最大提升约42%,F₁值最大提升约33%。

关键词: 异构网络, 社交网络, 兴趣模型, 非负矩阵分解, 标签传播

Abstract:

Due to great advances in the mobile Internet, the Social Network Service (SNS) has become an indispensable service. The development of current mainstream social media shows a trend that social service and information service are combined and interwork to provide a better experience. Meanwhile there is increasing polarization among users. The heterogeneous features, say, the combination of information content and sociality as well as the polarization of user roles, present challenges to traditional research in social media. Some studies of social media are mainly based on the equal position among nodes or similar relations. If the algorithms brought about by these studies are applied directly to the networks where the users are highly polarized, the results may be distorted or even be quite different from the fact. A new model for interests mining based on social relations is proposed in this paper. Dealing with the polarization in social media, we incorporate matrix factorization and the label propagation algorithm to treat information disseminators and average users, respectively, in order to discover interests of average users in a large-scale heterogeneous network. The validness of the model and the performance and advantages of the algorithm are tested and verified in Zhihu datasets. Experiments show that the maximum increase in the recall of the proposed method, compared with the baseline, is 42%.

Key words: heterogeneous network, social networks, interest model, non-negative matrix factorization, label Propagation

中图分类号:

TP391.1

屠守中,闫洲,卫玲蔚,朱小燕. 异构社交网络用户兴趣挖掘方法[J]. 西安电子科技大学学报, 2019, 46(2): 83-88.

TU Shouzhong,YAN Zhou,WEI Lingwei,ZHU Xiaoyan. User interesting mining method in the heterogeneous social network[J]. Journal of Xidian University, 2019, 46(2): 83-88.

图/表 2

参考文献 17

[1]	MISLOVE A, MARCON M, GUMMADI K P , et al. Measurement and Analysis of Online Social Networks[C]// Proceedings of the 2007 ACM SIGCOMM Internet Measurement Conference. New York: ACM, 2007: 29-42.
[2]	GASPARETTI F . Modeling User Interests from Web Browsing Activities[J]. Data Mining and Knowledge Discovery, 2017,31(2):502-547. doi: 10.1007/s10618-016-0482-x
[3]	DENG L, JIA Y, ZHOU B , et al. User Interest Mining via Tags and Bidirectional Interactions on Sina Weibo[J]. World Wide Web-internet and Web Information Systems, 2018,21(2):515-536. doi: 10.1007/s11280-017-0469-6
[4]	TU S Z, HUANG M L . Mining Microblog User Interests Based on Text Rank with TF-IDF Factor[J]. Journal of China Universities of Posts and Telecommunications, 2016,23(5):40-46. doi: 10.1016/S1005-8885(16)60056-0
[5]	PIAO S, WHITTLE J . A Feasibility Study on Extracting Twitter Users’ Interests Using NLP Tools for Serendipitous Connections[C]// Proceedings of the 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing. Washington: IEEE Computer Society, 2011: 910-915.
[6]	BHARGAVA P, BRDICZKA O, ROBERTS M . Unsupervised Modeling of Users’ Interests from Their Facebook Profiles and Activities[C]// Proceedings of the 2015 International Conference on Intelligent User Interfaces. New York: ACM, 2015: 191-201.
[7]	LIM K H, DATTA A . Interest Classification of Twitter Users Using Wikipedia[C]// Proceedings of the 2013 9th International Symposium on Open Collaboration. New York: ACM, 2013: 22.
[8]	GUO Q J, JI W T, ZHOU R Y . Algorithm Study under Big Data Environment of Personalized Recommendation Based on User Interest Model[C]// Proceedings of the 2017 16th IEEE/ACIS International Conference on Computer and Information Science. Piscataway: IEEE, 2017: 167-172.
[9]	HU B, ESTER M . Social Topic Modeling for Point-of-interest Recommendation in Location-based Social Networks[C]// Proceedings of the 2014 IEEE International Conference on Data Mining. Piscataway: IEEE, 2014: 845-850.
[10]	TANG J Y, LIU Z Y, SUN M S . Measuring and Visualizing Interest Similarity between Microblog Users[C]// Lecture Notes in Computer Science: 7923. Heidelberg: Springer Verlag, 2013: 478-489.
[11]	YANG C F, ZHOU Y P, CHIU D M. Who Are Like-minded:Mining User Interest Similarity in Online Social Networks[C]// Proceedings of the 2016 10th International Conference on Web and Social Media. Palo Alto: AAAI Press, 2016: 731-734.
[12]	HE R N, MCAULEY J. . Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation[C]// Proceedings of the 2017 IEEE International Conference on Data Mining. Piscataway: IEEE, 2017: 191-200.
[13]	KIM Y, SHIM K. A Recommendation System for Twitter Using Probabilistic Modeling[C]// Proceedings of the 2011 IEEE International Conference on Data Mining. Piscataway: IEEE, 2011: 340-349.
[14]	高明, 金澈清, 钱卫宁 , 等. 面向微博系统的实时个性化推荐[J]. 计算机学报, 2014,37(4):963-975. doi: 10.3724/SP.J.1016.2014.00963
	GAO Ming, JIN Cheqing, QIAN Weining , et al. Real-time Personalized Recommendation for Microblog System[J]. Chinese Journal of Computers, 2014,37(4):963-975. doi: 10.3724/SP.J.1016.2014.00963
[15]	LEE D D, SEUNG H S , Algorithms for Non-negative Matrix Factorization[C]// Advances in Neural Information Processing Systems. Vancouver, Canada: Neural Information Processing Systems Foundation, 2000: 535-541.
[16]	XU W, LIU X, GONG Y H , Document Clustering Based on Non-negative Matrix Factorization[C]// Proceedings of the 2003 SIGIR Forum. New York: ACM, 2003: 267-273.
[17]	ZHOU D Y, BOUSQUET O, LAL T N, et al. Learning with Local and Global Consistency[C]// Advances in Neural Information Processing Systems. Vancouver, Canada: Neural Information Processing Systems Foundation, 2003: 321-328.

参数	基于NMF的标签传播算法	基于LDA的标签传播算法	NMF+SVM算法	邻居投票算法
查准率	67.83	59.01	50.47	62.71
查全率	68.87	47.93	26.71	31.91
F₁	68.35	52.90	34.93	42.30