JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites
Goto, Masayuki; Mikawa, Kenta; Hirasawa, Shigeichi; Kobayashi, Manabu; Suko, Tota; Horii, Shunsuke;
  PDF(new window)
 Abstract
The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.
 Keywords
Web Marketing;Big Data;Latent Class Model;Aspect Model;Customer Segmentation;Business Analytics;
 Language
English
 Cited by
 References
1.
Bhatnagar, A. and Ghose, S. (2004), A Latent Class Segmentation Analysis of E-Shoppers, Journal of Business Research, 57(7), 758-767. crossref(new window)

2.
Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer.

3.
Breiman, L., Friedman, J. H. Olshen, R. A., and Sone, C. J. (1984), Classification and Regression Trees, Wadsworth.

4.
Curry, J. and Curry, A. (2000), The Customer Marketing Method, Free Press.

5.
Dempster, A., Laird, N., and Rubin, D. (1977), Maximum Likelihood from Incomplete Data via the EM Algorithm, J. Royal Statist. Soc., Series B, 39(1), 1-38.

6.
Fujiwara, N., Mikawa, K., and Goto, M. (2014), A New Estimation Method of Latent Class Model with High Accuracy by Using Both Browsing and Purchase Histories, The 15th Asia Pacific Industrial Engineering and Management Systems Conference, APIEMS.

7.
Goto, M., Minetoma, K., Mikawa, K., Kobayashi, M., and Hirasawa, S. (2014), A Modified Aspect Model for Simulation Analysis, IEEE International Conference on Systems, Man, and Cybernetics.

8.
Green, P. E., Carmone, F. J., and Wachspress, D. P. (1976), Consumer Segmentation Via Latent Class Analysis, Journal of Consumer Research, 3(3), 170-174. crossref(new window)

9.
Hofmann, T. (1999), Probabilistic Latent Semantic Indexing, The 22nd Annual International SIGIR Conference on Research and Development in Information Retrieval.

10.
Hofmann, T. and Puzicha, J. (1999), Latent Class Models for Collaborative Filtering, Proc. 16th International Joint Conference on Artificial Intelligence, 688-693.

11.
Hofmann, T. (2004), Gaussian Latent Semantic Models for Collaborative Filtering, Proc. the 26th Annual International ACM SIGIR Conference, 22(1), 259-266.

12.
Hofmann, T. (2004), Latent Semantic Models for Collaborative Filtering, ACM Trans. Information Systems, 22(1), 89-115. crossref(new window)

13.
Hughes, A. M. (2006), Strategic Database Marketing, McGraw-Hill.

14.
Jin, R. Si, L., and Zhai, C. X. (2003), Preference-based Graphic Models for Collaborative Filtering, UAI Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, 329-336.

15.
Jin, R., Si, L., and Zhai, C. (2006), A Study of Mixture Models for Collaborative Filtering," Journal of Information Retrieval, 9(3), 357-382, DOI 10.1007/s10791-006-4651-1. crossref(new window)

16.
Joint Association Study Group of Management Science in Japan (2014), http://jasmac-j.jimdo.com/.

17.
Langseth, H. and Nielsen, T. D. (2011), A Latent Model for Collaborative Filtering, preprint submitted to Elsevier.

18.
Magidson, J. and Vermunt, J. K. (2002), Latent Class Models for Clustering: A Comparison with K-means, Canadian Journal of Marketing Research, 20 (1), 37-44.

19.
McLachlan, G. and Krishnan, T. (2007), The EM Algorithm and Extensions, Wiley-Interscience.

20.
Oi, T., Mikawa, K., and Goto, M. (2013), A Study of Recommender Systems Based on the Latent Class Model Estimated by Combining Both Evaluation and Purchase Histories, The 14th Asia Pacific Industrial Engineering and Management Systems Conference, APIEMS.

21.
Si, L. and Jin, R. (2003), Flexible Mixture Model for Collaborative Filtering, Proc. 20th International Conference on Machine Learning, 2, 704-711.

22.
Sitkrongwong, P., Maneeroj, S., and Takasu, A. (2013), Latent Probabilistic Model for Context-Aware Recommendations, IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), DOI 10.1109/WI-IAT.2013.14. crossref(new window)

23.
Suzuki, T., Kumoi, G., Mikawa, K., and Goto, M. (2014), A Design of Recommendation Based on Flexible Mixture Model Considering Purchasing Interest and Post-Purchase Satisfaction, Journal of Japan Industrial Management Association, 64(4E), 570-578.

24.
Swait, J. and Adnmowicz, W. (2001), Consumer Choice: A Latent Class Model of Decision Strategy Switching, Journal of Consumer Research, 28(1), 135-148. crossref(new window)

25.
Train, K. E. (2009), Discrete Choice Methods with Simulation-Second edition, Cambridge University Press.