中文摘要：

通过分析电子商务网站的Web服务器日志提取网络消费者的商品浏览行为信息,利用消费者的浏览频率、浏览时间、链接路径数及路径深度估计商品对网络消费者当前浏览期间偏好的影响权重.结合双向关联规则理论和Apriori算法挖掘具有相互依赖关系的商品,找出网络消费者的商品偏好浏览路径,根据消费者当前的浏览行为发现其可能感兴趣的商品,并进一步计算消费者对商品的偏好程度.最后利用自主开发的旅游电子商务网站的Web日志数据进行仿真实验,挖掘网络消费者的旅游偏好.实验结果表明,在相同的实验条件下,与基于关联规则的偏好挖掘方法相比,基于双向关联规则的偏好挖掘方法的推荐精度增加,推荐覆盖率扩大.

英文摘要：

By analysis of web server logs in e--commerce to extract the information of web consumer＇s products browsing behavior, the weight of a product of web consumer＇s current preference has been estimated by means of consumer＇s browsing frequency, browsing time, the number of paths and links depth. The theory of bidirectional association rules was comhined with the idea of Apriori to find out interdependent products with to discover consumer＇s product preference path, candidate products of consumer preferred have been found based on consumer＇ s current browsing behavior, and then each preference degree of consumer to those products has been calculated respectively. Finally, the web server logs of a self--developed e--commerce web site has been used in the simulation to find out web consumers tour preference. Experimental results shows that the recommendation accuracy of preference mining method based on bidirectional association rules has improved greatly and the coverage has been expanded compared with preference mining method based on association rules when the experimental condition is equal.