A Method of Commodity Recommendation Based on Customer Shopping Model of Bayesian Network
Ji Jun · Jisuanji yingyong yanjiu · 2005
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 action. 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.