Bayesian Networks Structure Learning and Its Application to Personalized Recommendation in a B2C Portal

Web Intelligence (WI) is a new and active research field in current AI and IT. Personalized recommendation in an intelligent B2C portal is an important research topic in WI. In this paper, we first investigate the architecture of a B2C portal from the viewpoint of conceptual levels of WI. Aiming at data mining of knowledge-level in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, making the best of lower order Conditional Independence (CI) tests and accelerating search process by means of sort order for parent nodes. By a number of experiments on ALARM datasets, we find that the proposed algorithm is both more efficient and effective than others. We have applied this algorithm to a commodity recommendation system in a B2C portal. Our experimental results demonstrate that the recommendation method based on a Customer Shopping Model (CSM) produced by the new algorithm outperforms some traditional ones in rates of coverage and precision.