Personalized recommendation based on item dependency map

In data mining we want to find hidden knowledge, unexpected pattern and new rule from massive data. In this paper, we intend to find user's item purchasing pattern and recommend goods that he/she wants. So we suggest item dependency map which express relation between purchased items. Using an algorithm that we suggest, we can recommend an item, which a user has not bought yet but maybe is likely to interested in. And item dependency map is used as patterns for association in hopfield network so we can extract users' global purchasing item pattern only using users' partial information. Hopfield network is an iterative auto-associative network consisting of a single layer of fully connected processing elements which can function as an associative memory. And hopfield network can extract global information from sub-information. Therefore this algorithm obtains an advantage of hopfield networks. Our algorithm can be applied to real web site and help web master to know users' taste.