A Commerce Recommender System for Improving Customer Relationship Management in Shopping Centres

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-13 Number-4
Year of Publication : 2014
Authors : Theresa Rani Joseph , Smitha Jacob


Theresa Rani Joseph , Smitha Jacob. "A Commerce Recommender System for Improving Customer Relationship Management in Shopping Centres", International Journal of Engineering Trends and Technology (IJETT), V13(4),158-165 July 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


The potential application of data mining in recommender systems is a widely researched topic. The paper proposes a commerce recommender system which provides shopping recommendations for improving customer relationship within a shopping centre. A dual recommender system has been designed which includes a Personalized Recommender System (PRS) and a Generic Recommender System (GRS). PRS provides personalized recommendations based on each user’s previous shopping patterns. GRS on the other hand makes use of a similarity measuring algorithm for recommending shops containing products similar to those in another shop. The work focuses on the data mining phase of KDD. The frequent pattern mining for each user as well as similarity measurement algorithm is performed on a prototype database for the shopping centre under consideration.


[1] C.Dennis, D.Marsland and T.Cockett, “Data Mining for Shopping Centres - Customer Knowlegde Management Framework,” in Journal of Knowledge Management, 2001.
[2] I.Richard, D.Foster and R.Morgan, “Brand Knowledge Management: Growing Brand Equity,” in Journal of Knowledgde Management, 1998.
[3] R. Agrawal and R. Srikant, “Fast Algorithm for Mining Association Rules,” in Proc. Int’l Conf. Very Large Databases, pp. 478-499, Sept.1994.
[4] Mohammed Javeed Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara and Wei Li “New Algorithms for Fast Discovery of Assoctiation Rules,” in Proc. Int’l Conf. Knowledge Discovery and Data Mining,1997.
[5] J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” in Proc. ACM SIGMOD Conf. Management of Data, pp. 1-12, May 2000.
[6] Eric Hsueh-Chan Lu, Wang-Chien Lee and Vincent S. Tseng, “A Framework for Personal Mobile Commerce Pattern Mining and Prediction,” in Proc. IEEE Transactions on Knowledge and Data Engineering, 2012.
[7] Y. Lu, “Concept Hierarchy in Data Mining: Specification, Generation and Implementation,” master’s thesis, Simon Fraser Univ., 1997.
[8] J. L. Herlocker, J.A. Konstan, A. Brochers, and J. Riedl, “An Algorithm Framework for Performing Collaborative Filtering,” in Proc. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 230-237, Aug. 1999.
[9] R.Agrawal and R.Srikant, “Mining Sequential Patterns” in Proc. Int’l Conf. Data Eng., pp. 3-14, Mar. 1995.
[10] L. Kaufman and P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, Mar. 1990.

Personalized Recommender System, Generic Recommender System, Frequent Pattern Mining, Similarity Measuring Model, Customer Relationship Management.