The Community for Technology Leaders
RSS Icon
Issue No.05 - May (2012 vol.24)
pp: 769-782
V. S-M Tseng , Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Due to a wide range of potential applications, research on mobile commerce has received a lot of interests from both of the industry and academia. Among them, one of the active topic areas is the mining and prediction of users' mobile commerce behaviors such as their movements and purchase transactions. In this paper, we propose a novel framework, called Mobile Commerce Explorer (MCE), for mining and prediction of mobile users' movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three major components: 1) Similarity Inference Model (SIM) for measuring the similarities among stores and items, which are two basic mobile commerce entities considered in this paper; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users' Personal Mobile Commerce Patterns (PMCPs); and 3) Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors. To our best knowledge, this is the first work that facilitates mining and prediction of mobile users' commerce behaviors in order to recommend stores and items previously unknown to a user. We perform an extensive experimental evaluation by simulation and show that our proposals produce excellent results.
user interfaces, data mining, inference mechanisms, mobile commerce, pattern classification, IEEE, personal mobile commerce, pattern mining, pattern prediction, user movement behavior, user mobile commerce behavior, purchase transaction behavior, mobile commerce explorer framework, similarity inference model, personal mobile commerce pattern mine, PMCP-Mine algorithm, mobile commerce behavior predictor, Mobile communication, Business, Data mining, Predictive models, Mobile computing, Transaction databases, Trajectory, mobile commerce., Data mining
V. S-M Tseng, "A Framework for Personal Mobile Commerce Pattern Mining and Prediction", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 5, pp. 769-782, May 2012, doi:10.1109/TKDE.2011.65
[1] R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rule between Sets of Items in Large Databases," Proc. ACM SIGMOD Conf. Management of Data, pp. 207-216, May 1993.
[2] R. Agrawal and R. Srikant, "Fast Algorithm for Mining Association Rules," Proc. Int'l Conf. Very Large Databases, pp. 478-499, Sept. 1994.
[3] R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proc. Int'l Conf. Data Eng., pp. 3-14, Mar. 1995.
[4] S.F. Altschul, W. Gish, W. Miller, E.W. Myers, and D.J. Lipman, "Basic Local Alignment Search Tool," J. Molecular Biology, vol. 215, no. 3, pp. 403-410, Oct. 1990.
[5] M.-S. Chen, J.-S. Park, and P.S. Yu, "Efficient Data Mining for Path Traversal Patterns," IEEE Trans. Knowledge and Data Eng., vol. 10, no. 2, pp. 209-221, Apr. 1998.
[6] J. Han and Y. Fu, "Discovery of Multiple-Level Association Rules in Large Database," Proc. Int'l Conf. Very Large Data Bases, pp. 420-431, Sept. 1995.
[7] J. Han and M. Kamber, Data Mining: Concepts and Techniques, second ed. Morgan Kaufmann, Sept. 2000.
[8] J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. ACM SIGMOD Conf. Management of Data, pp. 1-12, May 2000.
[9] J.L. Herlocker, J.A. Konstan, A. Brochers, and J. Riedl, "An Algorithm Framework for Performing Collaborative Filtering," Proc. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 230-237, Aug. 1999.
[10] Y. Ishikawa, Y. Tsukamoto, and H. Kitagawa, "Extracting Mobility Statistics from Indexed Spatio-Temporal DataSets," Proc. Workshop Spatio-Temporal Database Management, pp. 9-16, Aug. 2004.
[11] G. Jeh and J. Widom, "SimRank: A Measure of Structural-Context Similarity," Proc. Int'l Conf. Knowledge Discovery and Data Mining, pp. 538-543, July 2002.
[12] H. Jeung, Q. Liu, H.T. Shen, and X. Zhou, "A Hybrid Prediction Model for Moving Objects," Proc. Int'l Conf. Data Eng., pp. 70-79, Apr. 2008.
[13] L. Kaufman and P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Mar. 1990.
[14] S.C. Lee, J. Paik, J. Ok, I. Song, and U.M. Kim, "Efficient Mining of User Behaviors by Temporal Mobile Access Patterns," Int'l J. Computer Science Security, vol. 7, no. 2, pp. 285-291, Feb. 2007.
[15] Y. Lu, "Concept Hierarchy in Data Mining: Specification, Generation and Implementation," master's thesis, Simon Fraser Univ., 1997.
[16] E. Modiano and A. Ephremides, "Efficient Algorithms for Performing Packet Broadcasts in a Mesh Network," IEEE/ACM Trans. Networking, vol. 4, no. 4, pp. 639-648, Aug. 1996.
[17] J.-S. Park, M.-S. Chan, and P.S. Yu, "An Effective Hash Based Algorithm for Mining Association Rules," Proc. ACM SIGMOD Conf. Management of Data, pp. 175-186, May 1995.
[18] J.M. Patel, Y. Chen, and V.P. Chakka, "Stripes: An Efficient Index for Predicted Trajectories," Proc. ACM SIGMOD Conf. Management of Data, pp. 635-646, June 2004.
[19] J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, "Mining Access Patterns Efficiently from Web Logs," Proc. Pacific Asia Conf. Knowledge Discovery and Data Mining, pp. 396-407, Apr. 2000.
[20] ShopKick, http://www.shopkick.comindex.html, 2010.
[21] Y. Tao, C. Faloutsos, D. Papadias, and B. Liu, "Prediction and Indexing of Moving Objects with Unknown motion patterns," Proc. ACM SIGMOD Conf. Management of Data, pp. 611-622, June 2004.
[22] Y. Tao, D. Papadias, and J. Sun, "The tpr∗-tree: An Optimized Spatio-Temporal Access Method for Predictive Queries," Proc. Int'l Conf. Very Large Data Bases, pp. 790-801, Sept. 2003.
[23] V.S. Tseng and W.C. Lin, "Mining Sequential Mobile Access Patterns Efficiently in Mobile Web Systems," Proc. Int'l Conf. Advanced Information Networking and Applications, pp. 867-871, Mar. 2005.
[24] V.S. Tseng and K.W. Lin, "Efficient Mining and Prediction of User Behavior Patterns in Mobile Web Systems," Information and Software Technology, vol. 48, no. 6, pp. 357-369, June 2006.
[25] V.S. Tseng, H.C. Lu, and C.H. Huang, "Mining Temporal Mobile Sequential Patterns in Location-Based Service Environments," Proc. Int'l Conf. Parallel and Distributed Systems, pp. 1-8, Dec. 2007.
[26] V.S. Tseng and C.F. Tsui, "Mining Multi-Level and Location-Aware Associated Service Patterns in Mobile Environments," IEEE Trans. Systems, Man and Cybernetics: Part B, vol. 34, no. 6, pp. 2480-2485, Dec. 2004.
[27] U. Varshney, R.J. Vetter, and R. Kalakota, "Mobile Commerce: A New Frontier," Computer, vol. 33, no. 10, pp. 32-38, Oct. 2000.
[28] J. Veijalainen, "Transaction in Mobile Electronic Commerce," Proc. Int'l Workshop Foundations of Models and Languages for Data and Objects, pp. 203-227, Sept. 1999.
[29] D. Xin, J. Han, X. Yan, and H. Cheng, "Mining Compressed Frequent-Pattern Sets," Proc. Int'l Conf. Very Large Data Bases, pp. 709-720, Aug. 2005.
[30] C.H. Yun and M.S. Chen, "Mining Mobile Sequential Patterns in a Mobile Commerce Environment," IEEE Trans. Systems, Man, and Cybernetics, Part C, vol. 37, no. 2, pp. 278-295, Mar. 2007.
[31] Y. Ye, Y. Zheng, Y. Chen, J. Feng, and X. Xie, "Mining Individual Life Pattern Based on Location History," Proc. Int'l Conf. Mobile Data Management Systems, Services and Middleware, pp. 1-10, May 2009.
[32] X. Yin, J. Han, and P.S. Yu, "LinkClus: Efficient Clustering via Heterogeneous Semantic Links," Proc. Int'l Conf. Very Large Data Bases, pp. 427-438, Aug. 2006.
[33] Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma, "Mining Interesting Location and Travel Sequences from GPS Trajectories," Proc. Int'l World Wide Web Conf., pp. 791-800, Apr. 2009.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool