CFP last date
22 April 2024
Reseach Article

Evaluating Techniques for Mining Customer Purchase Behavior and Product Recommendation- A Survey

by Ashmin Kaul, Mansi Virani, Teja Gummalla, Chaitanya Kaul
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 126 - Number 5
Year of Publication: 2015
Authors: Ashmin Kaul, Mansi Virani, Teja Gummalla, Chaitanya Kaul
10.5120/ijca2015906051

Ashmin Kaul, Mansi Virani, Teja Gummalla, Chaitanya Kaul . Evaluating Techniques for Mining Customer Purchase Behavior and Product Recommendation- A Survey. International Journal of Computer Applications. 126, 5 ( September 2015), 10-14. DOI=10.5120/ijca2015906051

@article{ 10.5120/ijca2015906051,
author = { Ashmin Kaul, Mansi Virani, Teja Gummalla, Chaitanya Kaul },
title = { Evaluating Techniques for Mining Customer Purchase Behavior and Product Recommendation- A Survey },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 126 },
number = { 5 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume126/number5/22547-2015906051/ },
doi = { 10.5120/ijca2015906051 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:16:41.738338+05:30
%A Ashmin Kaul
%A Mansi Virani
%A Teja Gummalla
%A Chaitanya Kaul
%T Evaluating Techniques for Mining Customer Purchase Behavior and Product Recommendation- A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 126
%N 5
%P 10-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Companies these days rely strongly upon their previous data history for predicting future trends in their business operations and strategies. Whether forecasting inventory or estimating sales, data mining has emerged as a new vibrant tool for extracting potential knowledge hidden inside the purchase behavior of the customer. Mining has made it possible to span over a large dataset in no time and come up with useful prediction. Building on the notion that customer’s purchase pattern plays a vital role in planning future strategies, in this paper, we focus upon several mining techniques used for understanding the customer’s purchase behavior. We analyze the purchase behavior from three different mining aspects i.e. classification, association rules, clustering technique and compare their accuracies. Thereafter, the study builds upon a scope whereby these techniques can be used to achieve a much filtered and unique dataset for analysis and also can be used for product recommendation for new customers. Finally, the study lays the scope of getting these techniques to be in sync with techniques used in forecasting such that companies can plan their sales and conduct the inventory management effectively.

References
  1. H. Gong and Q. Xia, “Study on application of customer segmentation based on data mining technology,” in 2009 ETP International Conference on Future Computer and Communication, 2009, pp. 167–170.
  2. X.-c. Yang, J. Wu, X.-h. Zhang, and T. jie Lu, “Using decision tree and association rules to predict cross selling opportunities,” in Machine Learning and Cybernetics, 2008 International Conference on, vol. 3. IEEE, 2008, pp. 1807–1811.
  3. A. S. Tewari, T. S. Ansari, and A. G. Barman, “Opinion based book recommendation using naive bayes classifier,” in Contemporary Computing and Informatics (IC3I), 2014 International Conference on. IEEE, 2014, pp. 139–144.
  4. J. Li, L. Zhang, F. Meng, and F. Li, “Recommendation algorithm based on link prediction and domain knowledge in retail transactions,” Procedia Computer Science, vol. 31, pp. 875–881, 2014.
  5. S.-h. Liao, Y.-j. Chen, and Y.-t. Lin, “Mining customer knowledge to implement online shopping and home delivery for hypermarkets,” Expert Systems with Applications, vol. 38, no. 4, pp. 3982–3991, 2011.
  6. P. Giudici and G. Passerone, “Data mining of association structures to model consumer behaviour,” Computational Statistics & Data Analysis, vol. 38, no. 4, pp. 533–541, 2002.
  7. B. Xiao, E. A¨ımeur, and J. M. Fernandez, “Pcfinder: An intelligent product recommendation agent for e-commerce.” in CEC, 2003, p. 181.
  8. C.-Y. Tsai and C.-C. Chiu, “A purchase-based market segmentation methodology,” Expert Systems with Applications, vol. 27, no. 2, pp. 265–276, 2004.
  9. X. Wang, J. Wang, T. Wang, H. Li, and D. Yang, “Parallel sequential pattern mining by transaction decomposition,” in Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on, vol. 4. IEEE, 2010, pp. 1746–1750.
  10. C. Wang and Y. Wang, “Discovering consumer’s behavior changes based on purchase sequences,” in Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on. IEEE, 2012, pp. 642–645.
  11. Y. Zuo, A. S. Ali, and K. Yada, “Consumer purchasing behaviour extraction using statistical learning theory,” Procedia Computer Science, vol. 35, pp. 1464–1473, 2014.
  12. A. Sato, T. Tamura, R. Huang, J. Ma, and N. Y. Yen, “Smart business services via consumer purchasing behaviour modeling,” in Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing. IEEE, 2013, pp. 812–817.
  13. T. Hong and E. Kim, “Segmenting customers in online stores based on factors that affect the customers intention to purchase,” Expert Systems with Applications, vol. 39, no. 2, pp. 2127–2131, 2012.
  14. S.-H. Liao, C.-M. Chen, and C.-H. Wu, “Mining customer knowledge for product line and brand extension in retailing,” Expert systems with Applications, vol. 34, no. 3, pp. 1763–1776, 2008.
  15. P. K. Bala, “Decision tree based demand forecasts for improving inventory performance,” in Industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on. IEEE, 2010, pp. 1926–1930.
  16. K. Sun and F. Bai, “Mining weighted association rules without preassigned weights,” Knowledge and Data Engineering, IEEE Transactions on, vol. 20, no. 4, pp. 489–495, 2008.
  17. E. T. Apeh, B. Gabrys, and A. Schierz, “Customer profile classification using transactional data,” in Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on. IEEE, 2011, pp. 37–43.
  18. G. Linden, B. Smith, and J. York, “Amazon. com recommendations: Item-to-item collaborative filtering,” Internet Computing, IEEE, vol. 7, no. 1, pp. 76–80, 2003.
  19. C. C. Aggarwal and P. S. Yu, “Outlier detection for high dimensional data,” in ACM Sigmod Record, vol. 30, no. 2. ACM, 2001, pp. 37–46.
  20. Y. Chen, S. Alspaugh, and R. Katz, “Interactive analytical processing in big data systems: A cross-industry study of mapreduce workloads,” Proceedings of the VLDB Endowment, vol. 5, no. 12, pp. 1802–1813, 2012.
  21. R. Agrawal, T. Imieli´nski, and A. Swami, “Mining association rules between sets of items in large databases,” in ACM SIGMOD Record, vol. 22, no. 2. ACM, 1993, pp. 207–216.
  22. K.-W. Cheung, J. T. Kwok, M. H. Law, and K.-C. Tsui, “Mining customer product ratings for personalized marketing,” Decision Support Systems, vol. 35, no. 2, pp. 231–243, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining classification association rules clustering purchase behavior.