User recommendation based on cross-platform online social networks

被引：0
|
作者：
Peng J. [1 ]
Wang T. [1 ]
Chen Y. [1 ]
Liu T. [2 ]
Xu W. [1 ]
机构：
[1] Computer Science School, Sichuan University, Chengdu
[2] College of Fundamental Education, Sichuan Normal University, Chengdu
来源：
Tongxin Xuebao/Journal on Communications | 2018年 / 39卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Cross-platform; Data mining; Online social networks; User recommendation;
D O I：
10.11959/j.issn.1000-436x.2018044
中图分类号：
学科分类号：
摘要：
In the field of online social networks on user recommendation, researchers extract users' behaviors as much as possible to model the users. However, users may have different likes and dislikes in different social networks. To tackle this problem, a cross-platform user recommendation model was proposed, users would be modeled all-sided. In this study, the Sina micro blog and the Zhihu were investigated in the proposed model, the experimental results show that the proposed model is competitive. Based on the proposed model and the experimental results, it can be known that modeling users in cross-platform online social networks can describe the user more comprehensively and leads to a better recommendation. © 2018, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：147 / 158
页数：11
相关论文
共 24 条
  • [1] Duan J., Ai Y., LDA topic model for microblog recommendation, International Conference on Asian Language Processing (IALP), pp. 185-188, (2015)
  • [2] Chen K.H., Han P.P., Wu J., User clustering based social network recommendation, Journal of Computer, 36, 2, pp. 349-359, (2013)
  • [3] Shang Y.M., Zhang P., Cao Y.N., New interest-sensitive and network-sensitive method for user recommendation, Journal on Communications, 36, 2, pp. 117-125, (2015)
  • [4] Zhong E., Fan W., Wang J., Et al., ComSoc: adaptive transfer of user behaviors over composite social network, The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 696-704, (2012)
  • [5] Moricz M., Dosbayev Y., Berlyant M., PYMK: friend recommendation at myspace, The 2010 ACM SIGMOD International Conference on Management of Data, pp. 999-1002, (2010)
  • [6] Deng Z., He B., Yu C., Et al., Personalized friend recommendation in social network based on clustering method, Computational Intelligence and Intelligent Systems, pp. 84-91, (2012)
  • [7] Liben-Nowell D., Kleinberg J., The link-prediction problem forsocial networks, Journal of the American Society for Information Science and Technology, 58, 7, pp. 1019-1031, (2007)
  • [8] Das A.S., Datar M., Garg A., Et al., Google news personalization: scalable online collaborative filtering, The 16th International Conference on World Wide Web, pp. 271-280, (2007)
  • [9] Kim Y., Shim K., TWILITE: a recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation, Information Systems, 42, 3, pp. 59-77, (2013)
  • [10] Abel F., Araujo S., Gao Q., Et al., Analyzing cross-system user modeling on the social Web, International Conference on Web Engineering, pp. 28-43, (2011)
123