Customer Segmentation Based on Context Preference Mining for Mobile Service

Abstract:

An increasing web services run in mobile context. Context may influence customer potential needs and buying behaviors in some specific situation. However existent customer segmentation method do not attach importance to context factors, which weakly support the development of context-sensitive mobile service . Therefore a mobile customer segmentation method based on context is proposed and validated by a case. The further research is mentioned in the end of the paper.

• Periodical: Advanced Materials Research (Volume 186)
• Pages: 348-352
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.186.348
• Online since: January 2011
• Authors: Mu Hai Hu, Shu Qin Cai, Ting Ting Tan
• Keywords: Context, Customer Sgementation, Hypergraph Partitioning, Mobile Service

References

[1] W. Smith, in: Product Differentiation and Market Segmentation. Alternative Marketing Strategies. volume 7 of Journal of Marketing(1956).

[2] M. Gorgoglione, C. Palmisano and A. Tuzhilin, in: Personalization in context: Does context matter when building personalized customer models, Proceedings of the Sixth International Conference on Data Mining, Boston(2006), in press.

DOI: 10.1109/icdm.2006.125

[3] J.Y. Choi, H.S. Song and S.H. Kim, in: MCORE: a context-sensitive recommendation system for the mobile Web. volume 24 of Expert Systems, Chapter, 1, (2007).

DOI: 10.1111/j.1468-0394.2007.00419.x

[4] L. Yingzi, W. Hao, in: A Summarization of Customer Segmentation Methods , volume 20 of Journal of Industrial Engineering and Engineering Management, Chapter, 1, (2006).

[5] J.E. Jackson, in: A user's guide to principal components. John Wiley & Sons Inc, New York(1991) , in press.

[6] U. Panniello, A. Tuzhilin. in: Experimental Comparison of Pre- vs. Post-filtering Approaches in Context-Aware Recommender Systems, ACM Conference On Recommender Systems archive Proceedings of the third ACM conference on Recommender systems, New York (2009).

DOI: 10.1145/1639714.1639764

[7] E.H. Han, G. Karypis, V. Kumar, and B. Mobasher. in: Clustering in a high-dimensional space using hypergraph models, Minneapolis : Department of Computer Science, University of Minnesota(1997) , in press.

[8] C. Jianbin, W. Shujing and S. Hantao, in: New Clustering Method in High-Dimensional Space Based on Hypergraph-Mode, volume 15 of Journal of Beijing Institute of Technology, Chapter, 2, (2006).

[9] B.N. Miller, I. Albert and S.K. Lam. in: MovieLens Unplugged: Experiences with a Recommender System on Four Mobile Devices, volume 17 of People and Computers, Chapter, 4, (2004).

DOI: 10.1007/978-1-4471-3754-2_16

[10] G. Karypis, R. Aggarwal and V. Kumar. in: Multilevel hypergraph partitioning application in VLSI design . Proceedings of the ACM/IEEE Design Automation Conference, ACM Press, CA(1997), in press.

DOI: 10.1109/dac.1997.597203