Efficient Pattern Mining and Prediction of User Behavior in Mobile Commerce

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-12 Number-6
Year of Publication : 2014
Authors : S.Kiruthika


S.Kiruthika. "Efficient Pattern Mining and Prediction of User Behavior in Mobile Commerce", International Journal of Engineering Trends and Technology (IJETT), V12(6),300-304 June 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


Mobile commerce has received a lot of interests from both of the industry and academia. A framework called Mobile Commerce Explorer for mining and prediction of mobile user’s movements and purchase transactions under the context of mobile commerce, consists of three major components , Similarity Inference Model (SIM), Personal mobile commerce pattern mine algorithm and Mobile Commerce Behavior Prediction. In the past only purchase data of users were used in recommender system before, while navigational and behavioral pattern data were not utilized. The method is to develop a recommender system based on navigation and behavior. First, all the data related to the purchase, navigational and behavioral patterns are gathered. The confidence levels obtained by the above analysis were used to determine a preference level for each pair of two products. The confidence levels between clicked products, between the products placed in the basket and between purchased products were calculated respectively and then, the preference level is estimated through the linear combination of above three confidence level. Here Random Walk with Restart (RWR) algorithm is used to retrieve items for recommendation to the user.


[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

Mobile Commerce, Data mining.