Exploring the sequential usage patterns of mobile Internet services based on Markov models

Abstract Mobile Internet has developed rapidly, and various types of mobile Internet services have changed people’s lifestyles profoundly. Consequently, there is a broad market for mobile Internet service providers. To provide better service and attract users, service providers must understand their users’ behavior patterns. This study proposes a framework to model users’ mobile online behavior based on a multi-state model and a hidden Markov model; this study also extracts typical sequential behavior patterns through clustering methods. The results of the experiments display several characteristic behavior patterns that can guide service providers in application designing, operating, and marketing.

[1]  L. R. Rabiner,et al.  A probabilistic distance measure for hidden Markov models , 1985, AT&T Technical Journal.

[2]  Manuele Bicego,et al.  A Hidden Markov Model-Based Approach to Sequential Data Clustering , 2002, SSPR/SPR.

[3]  Wang-Chien Lee,et al.  A Framework for Personal Mobile Commerce Pattern Mining and Prediction , 2012, IEEE Transactions on Knowledge and Data Engineering.

[4]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[5]  Zoubin Ghahramani,et al.  An Introduction to Hidden Markov Models and Bayesian Networks , 2001, Int. J. Pattern Recognit. Artif. Intell..

[6]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[7]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[8]  Chuan Xu,et al.  Modeling Web Browsing on Mobile Internet , 2011, IEEE Communications Letters.

[9]  Taeho Hong,et al.  Segmenting customers in online stores based on factors that affect the customer's intention to purchase , 2012, Expert Syst. Appl..

[10]  Zhao Guo.  Revealing Service Visit Characteristics in Mobile Internet , 2013 .

[11]  Hermann Ney,et al.  The Alignment Template Approach to Statistical Machine Translation , 2004, CL.

[12]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[13]  Roung-Shiunn Wu,et al.  Customer segmentation of multiple category data in e-commerce using a soft-clustering approach , 2011, Electron. Commer. Res. Appl..

[14]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[15]  Bart Baesens,et al.  A dynamic understanding of customer behavior processes based on clustering and sequence mining , 2014, Expert Syst. Appl..

[16]  Chuan Xu,et al.  Revealing Service Visit Characteristics in Mobile Internet: Revealing Service Visit Characteristics in Mobile Internet , 2014 .

[17]  Xi Chen,et al.  Exploring business opportunities from mobile services data of customers: An inter-cluster analysis approach , 2010, Electron. Commer. Res. Appl..

[18]  J. Kalbfleisch,et al.  The Analysis of Panel Data under a Markov Assumption , 1985 .

[19]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[20]  Lixin Gao,et al.  Profiling users in a 3g network using hourglass co-clustering , 2010, MobiCom.

[21]  Fei Li,et al.  A server's perspective of Internet streaming delivery to mobile devices , 2012, 2012 Proceedings IEEE INFOCOM.

[22]  Jiawei Han,et al.  CLARANS: A Method for Clustering Objects for Spatial Data Mining , 2002, IEEE Trans. Knowl. Data Eng..

[23]  H. D. Miller,et al.  The Theory Of Stochastic Processes , 1977, The Mathematical Gazette.

[24]  J. T. Curtis,et al.  An Ordination of the Upland Forest Communities of Southern Wisconsin , 1957 .

[25]  P Hougaard,et al.  Multi-state Models: A Review , 1999, Lifetime data analysis.

[26]  Carmen Cadarso-Suárez,et al.  Multi-state models for the analysis of time-to-event data , 2009, Statistical methods in medical research.

[27]  Tzung-Shi Chen,et al.  Mining User Movement Behavior Patterns in a Mobile Service Environment , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[28]  Hein Putter,et al.  Software for multistate analysis , 2014 .

[29]  Sung-Hyuk Cha.  Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[30]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[31]  Lusheng Ji,et al.  Characterizing geospatial dynamics of application usage in a 3G cellular data network , 2012, 2012 Proceedings IEEE INFOCOM.

[32]  Peter Haan,et al.  A Multi-state model of state dependence in labor supply: Intertemporal labor supply effects of a shift from joint to individual taxation , 2010 .

[33]  Li-Chen Cheng,et al.  Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study , 2012, Electron. Commer. Res. Appl..

[34]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[35]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[36]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[37]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[38]  Christopher Jackson,et al.  Multi-state modelling with R: the msm package , 2014 .

[39]  Christopher H. Jackson,et al.  Multi-State Models for Panel Data: The msm Package for R , 2011 .

[40]  N. K. Shankaranarayanan,et al.  Modeling and characterization of large-scale Wi-Fi traffic in public hot-spots , 2011, 2011 Proceedings IEEE INFOCOM.

[41]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[42]  Mario Stanke,et al.  Gene prediction with a hidden Markov model and a new intron submodel , 2003, ECCB.