%0 Journal Article
%T A Method of Commodity Recommendation-Based on Customer Shopping Model of Bayesian Network<br>一种基于贝叶斯网客户购物模型的商品推荐方法*
%A JI Jun-zhong
%A SHA Zhi-qiang
%A LIU Chun-nian
%A <br>冀俊忠
%A 沙志强
%A 刘椿年
%J 计算机应用研究
%D 2005
%I 
%X 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.
%K Web Mining
%K Bayesian Network
%K Customer Model
%K Individual Recommendation<br>Web挖掘
%K 贝叶斯网
%K 客户购物模型
%K 个性化推荐
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=1D695A8A838E9A88466C554E94F192C1&yid=2DD7160C83D0ACED&vid=BC12EA701C895178&iid=E158A972A605785F&sid=8BD23BD67BF01A5C&eid=68D88C2FCF9C3098&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=0