By analysis of web server logs in e-commerce to extract the information of web consumers products browsing behavior

the weight of a product of web consumers current preference has been estimated by means of consumers browsing frequency

browsing time

the number of paths and links depth. The theory of bidirectional association rules was combined with the idea of Apriori to find out interdependent products with to discover consumers product preference path

candidate products of consumer preferred have been found based on consumers 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.