A Novel User Preference Prediction Model Based on Local User Interaction Network Topology

  • Siqing YouEmail author
  • Li Zhou
  • Yan Liu
  • Hongjie Liu
  • Fei XueEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)


As people’s decisions are influenced by their social relationships, social networks have been widely applied in user behavior analysis, preference prediction and recommendation. However, static social relationship in a network alone is insufficient to model interpersonal influence and predict user preferences. In this paper, we propose a local user interaction network topology (LUINT) model to calculate the social influence between neighbors, which takes into account three types of user interactions: “at” action, comment, and re-tweet. Moreover, we design and adopt a shortest path with maximum propagation (SPWMP) algorithm to model the influence propagation within the network. To evaluate our approach, experiments on data set KDD Cup 2012, Track 1 are conducted. The results indicate that the proposed model significantly outperforms the other benchmark methods in predicting preference of the users.


User interaction Social influence and propagation User behavior analysis Preference prediction Social network 


  1. 1.
    Agrawal, D., Budak, C., El, A.A, Georgiou, T., Yan, X.F.: Big data in online social networks: user interaction analysis to model user behavior in social networks. In: Databases in Networked Information Systems, pp. 1–16 (2014)Google Scholar
  2. 2.
    Agichtein, E., Brill, E., Dumais, S., Ragno, R.: Learning user inter-action models for predicting web search result preferences. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–10 (2006)Google Scholar
  3. 3.
    Fischer, E., Reuber, A.R.: Social interaction via new social media: (How) can interactions on Twitter affect effectual thinking and behavior? J. Bus. Ventur. 26(1), 1–18 (2011)Google Scholar
  4. 4.
    Farzaneh, G.B., Masoud, A., Heshaam F.: Impact of context on social influence. In: International Conference on Electrical Engineering, pp. 1–6 (2016)Google Scholar
  5. 5.
    Young, M.: The Technical Writer’s Handbook. University Science, Mill Valley (1989)Google Scholar
  6. 6.
    Junming, H., Xueqi, C., Jiafeng, G., Huawei, S., Kun, Y.: Social recommendation with interpersonal influence. In: European Conference on Artificial Intelligence, vol. 10, pp. 601–606 (2010)Google Scholar
  7. 7.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weight-ed networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)Google Scholar
  8. 8.
    Zhang, W., Lim, C., Sreenivasan, S., Xie, J., Szymanski, B.K., Kor-niss, G.: Social influencing and associated random walk models: asymptotic consensus times on the complete graph. Chaos Interdisc. J. Nonlinear Sci. 21(2), 025115 (2011)Google Scholar
  9. 9.
    Meeyoung, C., Hamed, H., Fabricio, B., Krishna, P.G.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 10–17 (2010)Google Scholar
  10. 10.
  11. 11.
    Liu, H., Hu, Z., Tian, H., Zhou, D.: An adaptive social influence propagation model based on local network topology. In: E-Commerce and Web Technologies, pp. 14–26 (2013)Google Scholar
  12. 12.
    Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE, pp. 492–508 (2004)Google Scholar
  13. 13.
    Richters, O., Peixoto, T.P.: Trust transitivity in social networks. PLoS ONE 6(4), e18384 (2011)Google Scholar
  14. 14.
    Golbeck, J., Parsia, B., Hendler, J.: Trust networks on the semantic web. Springer (2003)Google Scholar
  15. 15.
    Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 1360–1380 (1973)Google Scholar
  16. 16.
    Scott, J.: Social network analysis. Sociology 22(1), 109–127 (1988)Google Scholar
  17. 17.
    Christakis, N.A., Fowler, J.H.: Connected: The Amazing Power of Social Networks and How They Shape Our Lives. HarperPress, London (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of InformationBeijing Wuzi UniversityBeijingChina
  2. 2.Beijing Advanced Innovation Center for Future Internet TechnologyBeijingChina
  3. 3.School of LogisticsBeijing Wuzi UniversityBeijingChina

Personalised recommendations