Affiliations: College of Computer Science and Technology, Beijing
University of Technology, Beijing Municipal Key Laboratory of Multimedia and
Intelligent Software Technology, Beijing 100022, China | Department of Information Engineering, Maebashi
Institute of Technology, 460-1 Kamisadori-Cho, Maebashi-City, 371-0816,
Japan
Abstract: Web Intelligence (WI) is a new and active research field in current
AI and IT. Intelligent B2C Portals are an important research topic in WI. In
this paper, we first investigate and analyze the architecture of a B2C portal
for personalized recommendation from the viewpoint of conceptual levels of WI.
Aiming at knowledge-level data mining in a B2C portal, we present a new
improved learning algorithm of Bayesian Networks, which consists of two major
contributions, namely, reducing Conditional Independence (CI) test costs by few
lower order CI tests and accelerating search process by means of sort order for
candidate parent nodes. Experimental results on benchmark ALARM data sets show
that the improved algorithm has high accuracy, and is more efficient in the
time performance than other algorithms. Finally, we apply this algorithm to
learning Customer Shopping Model (CSM) in an intelligent recommendation system.
By a number of experiments on real world data, we find that the recommendation
method based on the learned CSM outperforms some traditional ones in rates of
coverage and precision.
