Skip to main content

On Using Temporal Networks to Analyze User Preferences Dynamics

  • Conference paper
  • First Online:
Discovery Science (DS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9956))

Included in the following conference series:

Abstract

User preferences are fairly dynamic, since users tend to exploit a wide range of information and modify their tastes accordingly over time. Existing models and formulations are too constrained to capture the complexity of this underlying phenomenon. In this paper, we investigate the interplay between user preferences and social networks over time. We propose to analyze user preferences dynamics with his/her social network modeled as a temporal network. First, we define a temporal preference model for reasoning with preferences. Then, we use evolving centralities from temporal networks to link with preferences dynamics. Our results indicate that modeling Twitter as a temporal network is more appropriated for analyzing user preferences dynamics than using just snapshots of static network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Li, J., Ritter, A., Jurafsky, D.: Inferring user preferences by probabilistic logical reasoning over social networks. arXiv preprint (2014). arXiv:1411.2679

  2. Abbasi, M.A., Tang, J., Liu, H.: Scalable learning of users’ preferences using networked data. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media, pp. 4–12. ACM (2014)

    Google Scholar 

  3. da Costa, L.F., Rodrigues, F.A., Travieso, G., Villas Boas, P.R.: Characterization of complex networks: a survey of measurements. Adv. Phys. 56(1), 167–242 (2007)

    Article  Google Scholar 

  4. Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519(3), 97–125 (2012)

    Article  Google Scholar 

  5. Holme, P.: Analyzing temporal networks in social media. Proc. IEEE 102(12), 1922–1933 (2014)

    Article  Google Scholar 

  6. Pereira, F.S.F., Amo, S., Gama, J.: Evolving centralities in temporal graphs: a twitter network analysis. In: First Workshop on High Velocity Mobile Data Management Co-Located with 17th IEEE International Conference on Mobile Data Management (MDM), pp. 43–48 (2016)

    Google Scholar 

  7. Arias, M., Arratia, A., Xuriguera, R.: Forecasting with twitter data. ACM Trans. Intell. Syst. Technol. (TIST) 5(1), 8 (2013)

    Google Scholar 

  8. Kapoor, K., Srivastava, N., Srivastava, J., Schrater, P.: Measuring spontaneous devaluations in user preferences. In: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1061–1069. ACM (2013)

    Google Scholar 

  9. Rafailidis, D., Nanopoulos, A.: Modeling the dynamics of user preferences in coupled tensor factorization. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 321–324. ACM (2014)

    Google Scholar 

  10. Yang, Z., Xue, J., Wilson, C., Zhao, B.Y., Dai, Y.: Process-driven analysis of dynamics in online social interactions. In: Proceedings of the 2015 ACM on Conference on Online Social Networks, pp. 139–149. ACM (2015)

    Google Scholar 

  11. Liu, F.: Preference change and information processing. Technical report, ILLC, University of Amsterdam (2006)

    Google Scholar 

  12. Liu, F.: Preference change a quantitative approach. Stud. Logic 2(3), 12–27 (2009)

    Google Scholar 

  13. Tang, J., Musolesi, M., Mascolo, C., Latora, V.: Temporal distance metrics for social network analysis. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 31–36. ACM (2009)

    Google Scholar 

  14. Guille, A., Hacid, H.: A predictive model for the temporal dynamics of information diffusion in online social networks. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 1145–1152. ACM (2012)

    Google Scholar 

  15. Tang, J., Musolesi, M., Mascolo, C., Latora, V., Nicosia, V.: Analysing information flows and key mediators through temporal centrality metrics. In: 3rd Workshop on Social Network Systems, p. 3. ACM (2010)

    Google Scholar 

  16. Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., Xu, Y.: Path problems in temporal graphs. Proc. VLDB Endowment 7(9), 721–732 (2014)

    Article  Google Scholar 

  17. Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., Latora, V.: Graph metrics for temporal networks. In: Holme, P., Saramäki, J. (eds.) Temporal Networks. Understanding Complex Systems, pp. 15–40. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  18. Koujaku, S., Kudo, M., Takigawa, I., Imai, H.: Community change detection in dynamic networks in noisy environment. In: 24th International Conference on World Wide Web Companion, pp. 793–798 (2015)

    Google Scholar 

  19. Liu, F.: Reasoning about Preference Dynamics. Synthese Library. Springer, Netherlands (2011)

    Book  MATH  Google Scholar 

  20. Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long-and short-term preference fusion. In: 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–732 (2010)

    Google Scholar 

  21. Wei, W., Carley, K.M.: Measuring temporal patterns in dynamic social networks. ACM Trans. Knowl. Disc. Data (TKDD) 10(1), 9 (2015)

    Google Scholar 

  22. Klochko, M.A., Ordeshook, P.C.: Endogenous Time Preferences in Social Networks. Edward Elgar Publishing, Cheltenham (2005)

    Google Scholar 

  23. Pereira, F.S.F.: Mining comparative sentences from social media text. In: Second Workshop on Interactions between Data Mining and Natural Language Processing Co-Located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 41–48 (2015)

    Google Scholar 

  24. Zafarani, R., Abbasi, M.A., Liu, H.: Social Media Mining: An Introduction. Cambridge University Press, New York (2014)

    Book  Google Scholar 

Download references

Acknowledgments

This work was supported by the research project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact / NORTE-01-0145-FEDER-000020”, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF) and by European Commission through the project MAESTRA (Grant number ICT-2013-612944). Fabiola Pereira is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. This work was also supported by the Brazilian Research Agencies CAPES and CNPq.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabíola S. F. Pereira .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Pereira, F.S.F., de Amo, S., Gama, J. (2016). On Using Temporal Networks to Analyze User Preferences Dynamics. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46307-0_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46306-3

  • Online ISBN: 978-3-319-46307-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics