Abstract
User segmentation is an essential element of marketing and product development that considers customers’ needs and recognizes the heterogeneity of those needs. In a key study of smartphone user segmentation, Lee et al. analyzed app usage sequencing using seq2seq architecture. However, despite achieving meaningful results, their approach could not provide a robust interpretation of user segmentation because the seq2seq architecture represented users in a continuous vector space generated from a black box model. In this paper, we propose an interpretable user representation method that combines app clustering with a novel segmentation method. The user representation clusters characteristically similar apps into common clusters, with each user represented by their frequencies of app use within their respective clusters. Two novel techniques are also applied to normalize the value of user representation based on the relative degrees of importance between app clusters and the membership strengths of individual apps within a cluster. Furthermore, to address the limitations of existing segmentation methods, in which the most closely located users are assigned to specific clusters, the proposed method segments represented users using a novel segmentation approach based on relative similarity. Experimental results demonstrate that the proposed method provides an intuitive interpretation for each user’s representation and segmentation results. Furthermore, we effectively show the similarities between the results produced by our method and ground truth and demonstrate that it outperforms existing user segmentation methods.
Similar content being viewed by others
References
Amiriparian, S., Freitag, M., Cummins, N., Schuller, B.: Sequence to sequence autoencoders for unsupervised representation learning from audio. In: Proceedings of the DCASE 2017 Workshop (2017)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), 10008 (2008)
Bose, I., Chen, X.: Exploring business opportunities from mobile services data of customers: an inter-cluster analysis approach. Electron. Commer. Res. Appl. 9(3), 197–208 (2010)
Brocardo, M.L., Traore, I., Saad, S., Woungang, I.: Authorship verification for short messages using stylometry. In: Computer, Information and Telecommunication Systems (CITS), 2013 International Conference on, IEEE, pp. 1–6 (2013)
Cheng, L.C., Sun, L.M.: Exploring consumer adoption of new services by analyzing the behavior of 3g subscribers: an empirical case study. Electron. Commer. Res. Appl. 11(2), 89–100 (2012)
Cichocki, A., Phan, A.H.: Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 92(3), 708–721 (2009)
Cravens, D.W., Piercy, N.: Strategic Marketing, vol. 7. McGraw-Hill, New York (2006)
d’Alessandro, C., Trucco, P.C.: Business potential and market opportunities of intelligent LBSS for personal mobility—a European case study. Proced. Comput. Sci. 5, 906–911 (2011)
De Reuver, M., Bouwman, H., De Koning, T.: The mobile context explored. In: Sath, S. (ed.) Mobile Service Innovation and Business Models, pp. 89–114. Springer, Berlin (2008)
Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: Proceedings of the 8th international conference on Mobile systems, applications, and services, ACM, pp. 179–194 (2010)
Févotte, C., Idier, J.: Algorithms for nonnegative matrix factorization with the \(\beta\)-divergence. Neural Comput. 23(9), 2421–2456 (2011)
Halko, N., Martinsson, P.G., Tropp, J.A.: Finding structure with randomness: stochastic algorithms for constructing approximate matrix decompositions (2009)
Hamka, F., Bouwman, H., De Reuver, M., Kroesen, M.: Mobile customer segmentation based on smartphone measurement. Telematics Inform. 31(2), 220–227 (2014)
Hu, Y.: Efficient, high-quality force-directed graph drawing. Math. J. 10(1), 37–71 (2005)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)
Insights, B., Insights, C.: Customer Segmentation. Bain & Company, Boston (2017)
Jang, M., Seo, S., Kang, P.: Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning. arXiv:1802.03238 (arXiv preprint) (2018)
Kotler, P., Armstrong, G.: Principles of Marketing. Pearson education, New York (2010)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. ICML 14, 1188–1196 (2014)
Lee, Y., Park, I., Cho, S., Choi, J.: Smartphone user segmentation based on app usage sequence with neural networks. Telemat. Inform. 20, 20 (2017)
Lin, Q.: Mobile customer clustering analysis based on call detail records. Commun. IIMA 7(4), 95 (2007)
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: Lstm-based encoder–decoder for multi-sensor anomaly detection. arXiv:1607.00148 (arXiv preprint) (2016)
McDaid, A.F., Murphy, B.T., Friel, N., Hurley, N.J.: Model-based clustering in networks with stochastic community finding. arXiv:1205.1997 (arXiv preprint) (2012)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (arXiv preprint) (2013)
Plaza, I., MartíN, L., Martin, S., Medrano, C.: Mobile applications in an aging society: status and trends. J. Syst. Softw. 84(11), 1977–1988 (2011)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)
Sell, A., Walden, P., Carlsson, C.: Are you efficient, trendy or skillfull? an exploratory segmentation of mobile service users. In: Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR), 2010 Ninth International Conference on. IEEE, pp. 116–123 (2010)
Shafiq, M.Z., Ji, L., Liu, A.X., Pang, J., Wang, J.: Characterizing geospatial dynamics of application usage in a 3g cellular data network. In: INFOCOM, 2012 Proceedings IEEE. IEEE, pp. 1341–1349 (2012)
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic dependency-based n-grams as classification features. In: Mexican International Conference on Artificial Intelligence. Springer, pp. 1–11 (2012)
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic dependency-based n-grams: more evidence of usefulness in classification. In: International Conference on Intelligent Text Processing and Computational Linguistics. Springer, pp. 13–24 (2013)
Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3(1), 583–617 (2002)
Tao, C.C., et al.: Market segmentation for mobile tv content on public transportation by integrating innovation adoption model and lifestyle theory. J. Serv. Sci. Manag. 1(03), 244 (2008)
Uronen, M.: Market segmentation approaches in the mobile service business. Master’s thesis, Helsinki University of Technology (2008)
Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: is a correction for chance necessary? In: Proceedings of the 26th annual international conference on machine learning. ACM, pp. 1073–1080 (2009)
Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11(Oct), 2837–2854 (2010)
Walsh, S.P., White, K.M., McD Young, R.: Needing to connect: the effect of self and others on young people’s involvement with their mobile phones. Aust. J. Psychol. 62(4), 194–203 (2010)
Waltman, L., van Eck, N.J.: A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86(11), 471 (2013)
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government(MSIT) (no. 2020-0795).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by T. Yao.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lee, Y., Cho, S. User segmentation via interpretable user representation and relative similarity-based segmentation method. Multimedia Systems 27, 61–72 (2021). https://doi.org/10.1007/s00530-020-00702-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-020-00702-4