Interest detection; Behaviour modelling; High utility events
Abstract :
[en] Mobile applications became the main interaction channel in several domains, such as banking. Consequently, understanding user behaviour on those apps has drawn attention in order to extract business-oriented outcomes. By combining Markov Chain and graph theory techniques, we successfully developed a process to model the app, to extract the click high utility events, to score the interest on those events and cluster the groups of interest. We tested our approach on an European bank dataset with over 3.5 millions of user's session. By implementing our approach, analysts can gain knowledge of user behaviour in terms of events that are important to the domain.
Disciplines :
Computer science
Author, co-author :
Ota, Fernando Kaway Carvalho
Damoun, Farouk ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
Lagraa, Sofiane
Becerra-Sanchez, Patricia
Hilger, Jean ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SnT Finnovation Hub
State, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
External co-authors :
yes
Language :
English
Title :
Event-Driven Interest Detection for Task-Oriented Mobile Apps
Publication date :
2022
Event name :
18th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
Event place :
Beppu, Japan
Event date :
November 8-11, 2021
By request :
Yes
Audience :
International
Main work title :
Mobile and Ubiquitous Systems: Computing, Networking and Services
Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–32 (1999). https://doi.org/10.1145/324133.324140
Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Tseng, V.S., Yu, P.S.: A survey of utility-oriented pattern mining. IEEE Trans. Knowl. Data Eng. 33(4), 1306–1327 (2021). https://doi.org/10.1109/TKDE.2019.2942594
Raphaeli, O., Goldstein, A., Fink, L.: Analyzing online consumer behavior in mobile and PC devices: a novel web usage mining approach. Electron. Commer. Res. Apps 26, 1–12 (2017)
Bucklin, R.E., Sismeiro, C.: Click here for internet insight: advances in clickstream data analysis in marketing. J. Interact. Mark. 23(1), 35–48 (2009)
Truong-Chi, T., Fournier-Viger, P.: A survey of high utility sequential pattern mining. In: Fournier-Viger, P., Lin, J.C.W., Nkambou, R., Vo, B., Tseng, V.S. (eds.) High-Utility Pattern Mining. SBD, vol. 51, pp. 97–129. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04921-8 4
Schneider, F., Feldmann, A., Krishnamurthy, B., Willinger, W.: Understanding online social network usage from a network perspective. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, pp. 35–48 (2009)
Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, pp. 49–62 (2009)
Borisov A, Wardenaar M, Markov I, De Rijke M. A click sequence model for web search. In: 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, pp. 45–54 (2018). https://doi.org/10.1145/3209978.3210004
Kumar, A., Salo, J., Li, H.: Stages of user engagement on social commerce platforms: analysis with the navigational clickstream data. Int. J. Electron. Commer. 23(2), 179–211 (2019)
Jindal, H., Sardana, N., Mehta, R.: Analysis and visualization of user navigations on web. In: Hemanth, J., Bhatia, M., Geman, O. (eds.) Data Visualization and Knowledge Engineering. LNDECT, vol. 32, pp. 195–221. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-25797-2 9
Scholtes, I.: When is a network a network? Multi-order graphical model selection in pathways and temporal networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. F1296(i), pp. 1037–1046 (2017). https://doi.org/10.1145/3097983.3098145
Husin, H.S., Seid, N.: Discovering users navigation of online newspaper using Markov model. In: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, pp. 1–4 (2017)
Wang, G., et al.: Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (2016)
Wang, Y.T., Lee, A.J.T.: Mining Web navigation patterns with a path traversal graph. Expert Syst. appl. 38(6), 7112–7122 (2011)
Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J.. Comput. 1(2), 146–160 (1972)
Bundy, A., Wallen, L.: Breadth-first search, In: Bundy, A., Wallen, L. (eds.) Catalogue of Artificial Intelligence Tools. Symbolic Computation (Artificial Intelli-gence), pp. 13–13. Springer, Heidelberg (1984). https://doi.org/10.1007/978-3-642-96868-6 25
Barbehenn, M.: A note on the complexity of Dijkstra’s algorithm for graphs with weighted vertices. IEEE Trans. Comput. 47(2), 263 (1998)
Chakraborty, S., Nagwani, N.K., Dey, L.: Performance comparison of incremental k-means and incremental DBSCAN algorithms. arXiv preprint arXiv:1406.4751 (2014)
Barbehenn, M.: A note on the complexity of Dijkstra’s algorithm for graphs with weighted vertices. IEEE Trans. Comput. 472, 263 (1998). https://doi.org/10.1109/12.663776
