Personalized recommendation based on a multilevel customer model
Personalized recommendation needs powerful Web Intelligence (WI) technologies to manage, analyze and employ various business data on the Web for e-business intelligence. This paper presents a novel recommendation framework on the Web, which is based on a multilevel customer model comprising three submodels, namely, the customer shopping model (CSM), the customer preference model (CPM), and the customer consumption model (CCM). These models capture a customer's information from different aspects. After preprocessing of raw data, we first build the CSM based on Bayesian networks by mining from customer shopping transactions, and then find the CPM by analyzing customer shopping history. Furthermore, the customer purchasing power can be formalized as a linear CCM. By combining the CSM with the present customer shopping action, a recommendation algorithm based on Bayesian probability inference is used to generate an individual recommendation set of commodities. A personalized filter including customization of the CPM and orientation of the CCM is also used to realize a more personalized recommendation. Experimental evaluation on real world data shows that the proposed approach can achieve personalized commodities recommendation efficiently and effectively.
personalized recommendation multilevel customer model web intelligence bayesian networks recommendation engine