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.
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References
Li, J., Ritter, A., Jurafsky, D.: Inferring user preferences by probabilistic logical reasoning over social networks. arXiv preprint (2014). arXiv:1411.2679
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)
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)
Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519(3), 97–125 (2012)
Holme, P.: Analyzing temporal networks in social media. Proc. IEEE 102(12), 1922–1933 (2014)
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)
Arias, M., Arratia, A., Xuriguera, R.: Forecasting with twitter data. ACM Trans. Intell. Syst. Technol. (TIST) 5(1), 8 (2013)
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)
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)
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)
Liu, F.: Preference change and information processing. Technical report, ILLC, University of Amsterdam (2006)
Liu, F.: Preference change a quantitative approach. Stud. Logic 2(3), 12–27 (2009)
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)
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)
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)
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)
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)
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)
Liu, F.: Reasoning about Preference Dynamics. Synthese Library. Springer, Netherlands (2011)
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)
Wei, W., Carley, K.M.: Measuring temporal patterns in dynamic social networks. ACM Trans. Knowl. Disc. Data (TKDD) 10(1), 9 (2015)
Klochko, M.A., Ordeshook, P.C.: Endogenous Time Preferences in Social Networks. Edward Elgar Publishing, Cheltenham (2005)
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)
Zafarani, R., Abbasi, M.A., Liu, H.: Social Media Mining: An Introduction. Cambridge University Press, New York (2014)
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.
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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
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DOI: https://doi.org/10.1007/978-3-319-46307-0_26
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