A Method of Commodity Recommendation-Based on Customer Shopping Model of Bayesian Network
一种基于贝叶斯网客户购物模型的商品推荐方法*

Presents a new recommendation framework based on customer shopping model. This framework formalizes the re-commending process as knowledge representation of the customer shopping information and uncertainty knowledge inference process. Firstly, this approach builds a customer model of Bayesian network by learning from customer shopping history data, then presents a recommendation algorithm based on probability inference in combination with customer present shopping ac-tion. Experimental results demonstrate that this method can effectively and in real-time generate an individual recommendation set of commodity, it is better than some traditional methods in rates of coverage and precision.