JACIII Vol.17 No.2 pp. 221-226
doi: 10.20965/jaciii.2013.p0221


Bayesian Network Model that Infers Purchase Probability in an Online Shopping Site

Yutaka Matsushita* and Syunsuke Maeda**

*Department of Psychological Informatics, Kanazawa Institute of Technology, 3-1 Yatsukaho, Hakusan, Ishikawa 924-0838, Japan

**Asobimo Incorporated, 5-26-19 Nishiikebukuro, Toshima-ku, Tokyo 171-0021, Japan

November 16, 2012
January 29, 2013
March 20, 2013
Bayesian network, inference, shopping site, product purchase, eye movements
In order to understand the properties of online shopping that contribute to visitors’ purchasing habits, we have developed a Bayesian network model that infers the probability of purchase from eye movement and web log data. The results obtained from this model imply that a short visit time on catalog pages and a high frequency of fixation on all pages are related to increased purchase probability. Furthermore, it is shown that websites conforming to Internet Usability Guidelines (IUG)make visitors feel little stress regardless of browsing patterns, and that websites not conforming to IUG require a very short visit time on catalog pages if low stress is to be maintained.
Cite this article as:
Y. Matsushita and S. Maeda, “Bayesian Network Model that Infers Purchase Probability in an Online Shopping Site,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.2, pp. 221-226, 2013.
Data files:
  1. [1] J. Nielsen, “Usability Engineering,” Morgan Kaufmann, 1994.
  2. [2] M. Khosravi and M. J. Tarokh, “Dynamic mining of users interest navigation patterns using naive Bayesian method,” IEEE Int. Conf. on Intelligent Computer Communication and Processing, pp. 119-122, 2010.
  3. [3] H. Adachi, I. Kuramoto, Y. Shibuya, and Y. Tsujino, “Finding users’ target pages based on web access logs,” SIG Technical Report, 2004-HI-112, No.9, pp. 35-42, 2005 (in Japanese).
  4. [4] K. Toda et al., “An information exploration model based on eye movements during browsing web pages,” SIG Technical Report, 2005-HI-113(6), No.52, pp. 35-42, 2005 (in Japanese).
  5. [5] Q. Ji, P. Lan, and C. Looney, “A probabilistic framework for modeling and real-time monitoring human fatigue,” IEEE Trans. Syst., Man, Cybern. Part A, Vol.36, No.5, pp. 862-875, 2006.
  6. [6] M. Yuasa, Y. Yasumura, and K. Nitta, “A tool for animated agents in network-based negotiation,” Proc. of 12th IEEE Int. Workshop on Robot and Human Interactive Communication. pp. 259-264, 2003.
  7. [7] K. Nomori et al., “Developing a computational model to predict infant behavior elicited by environmental factors,” The Japanese J. of Ergonomics, Vol.46, No.2, pp. 166-171, 2010 (in Japanese).
  8. [8] T. Furuhashi, “Statistics, multivariate analysis, and soft computing: toward analysis of systems with ultra many degrees of freedom,” Kyoritsu Publication, 2012 (in Japanese).
  9. [9] K. Shigemasu, M. Ueno, and Y. Motomura, “Survey of Bayesian Networks,” Baifu-kan, 2006 (in Japanese).
  10. [10] M. Stone, “Cross-validation and multinomial prediction,” Biometrica, Vol.61, No.3, pp. 509-515, 1974.

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