An improved Bayesian network structure learning algorithm and its application in an intelligent B2C portal

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.