The Knowledge Map Analysis of User Profile Research Based on CiteSpace

  • Danbei Pan
  • Hua YinEmail author
  • Yang Wang
  • Zhijian Wang
  • Zhensheng Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)


With the development of big data technology, user profile, as an effective method for delineating user characteristics, has attracted extensive attention from researchers and practitioners. Rich related literatures have been accumulated. How to find the key factors and the new direction from such a big library is a difficult problem for a new researcher entering the field. The knowledge map can be used to visualize the development trend, the frontier field and the overall knowledge structure from these researches. Therefore, we choose web of science database as the literature search engine and use CiteSpace to construct the user profile knowledge map. Through these maps, we analyze the important authors and countries, make the common word analysis and co-citation analysis, study the hot spots and important literatures. The time distribution shows that some foundational theories in user profile were produced at the second stage from 2004 to 2013. What’s more, from the geographical distribution, we find that user profile, as an abstract concept, has no unified framework. Each country focuses on the different research points. From the knowledge map of keywords, we find that the top three algorithmic techniques used in constructing user profile are clustering, classification, and collaborative filtering. At the same time, user profile is also used in some specific applications, such as anomaly detection, behavior analysis, and information retrieval.


User profile CiteSpace Knowledge map Visualization Big data 



This work was supported by Science and Technology Program of Guangzhou, China (No. 201707010495), Foundation for Distinguished Young Talents in Higher Education of Guangzhou, China (No. 2013LYM0032), Project supported by Guangdong Province Universities, China (No. 2015KTSCX046), and Foundation for Technology Innovation in Higher Education of Guangdong Province, China (No. 2013KJCX0085).


  1. 1.
    Chen, C.L.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275(11), 314–347 (2014)CrossRefGoogle Scholar
  2. 2.
    Liu, W., Liu, J., Shi, C., et al.: User network profile of behavior. Publishing House of Electronics Industry, Beijing (2016)Google Scholar
  3. 3.
    Zhu, H., Chen, E., Xiong, H., et al.: Mining mobile user preferences for personalized context-aware recommendation. ACM Trans. Intell. Syst. Technol. 5(4), 1–27 (2014)CrossRefGoogle Scholar
  4. 4.
    Marquardt, J., Farnadi, G., Vasudevan, G., et al.: Age and gender identification in social media. In: Proceedings of CLEF 2014 Evaluation Labs, pp. 1129–1136 (2014)Google Scholar
  5. 5.
    Mueller, J., Stumme, G.: Gender inference using statistical name characteristics in Twitter. In: MISNC, SI, DS (2016)Google Scholar
  6. 6.
    Chen, Y., Chen, C.M., Liu, Z.Y., et al.: The methodology function of cite space mapping knowledge domains. Stud. Sci. Sci. 33, 242–253 (2015)Google Scholar
  7. 7.
    Mayr, P., Scharnhorst, A.: Scientometrics and information retrieval: weak-links revitalized. Scientometrics 102(3), 2193–2199 (2014)CrossRefGoogle Scholar
  8. 8.
    Chen, C.: CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature. Wiley, Hoboken (2006)Google Scholar
  9. 9.
    Cui, Y., Mou, J., Liu, Y.: Knowledge mapping of social commerce research: a visual analysis using CiteSpace. Electron. Commer. Res. 18, 837 (2018)CrossRefGoogle Scholar
  10. 10.
    Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Kluwer Academic Publishers, Dordrecht (1997)Google Scholar
  11. 11.
    Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. User Model. User-Adap. Inter. 17(3), 217–255 (2007)CrossRefGoogle Scholar
  12. 12.
    Maleszka, M., Mianowska, B., Nguyen, N.T.: A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles. Knowl.-Based Syst. 47(3), 1–13 (2013)CrossRefGoogle Scholar
  13. 13.
    Chen, Y., Chen, C., Hu, Z.: Principles and Applications of Analyzing a Citation Space. Science Press, Beijing (2014)Google Scholar
  14. 14.
    Hu, Z., Chen, C., Liu, Z.: The recurrence of citations within a scientific article. In: The International Society of Scientometrics and Informetrics Conference, 29 June–July 2015Google Scholar
  15. 15.
    Middleton, S.E., Shadbolt, N.R., Roure, D.C.D.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)CrossRefGoogle Scholar
  16. 16.
    Mislove, A., Viswanath, B., Gummadi, K.P., et al.: You are who you know: inferring user profiles in online social networks. In: DBLP, pp. 251–260 (2010)Google Scholar
  17. 17.
    Xu, S., Bao, S., Fei, B., et al.: Exploring folksonomy for personalized search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, Singapore, 20–24 July 2008, pp. 155–162 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Danbei Pan
    • 1
  • Hua Yin
    • 1
    Email author
  • Yang Wang
    • 1
  • Zhijian Wang
    • 1
  • Zhensheng Hu
    • 1
  1. 1.Information SchoolGuangdong University of Finance and EconomicsGuangzhouChina

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