An Improved Item-based Collaborative Filtering Algorithm with Distinction between User's Long and Short Interests
YU Xue-li · Journal of Zhengzhou University · 2010
Collaborative filtering(CF) algorithms have been successfully used in many applications.But they have some problems,such as scalability,lower precision.And many improved CF algorithms have been proposed.However,all of them can't make a clear distinction between user's long and short interests for improving recommendation precision.In order to solve this problem,an improved weight strategy,consociating user's long and short interests data weighting method is introduced.The key of this method is how to identify user's long interests.So,two user's long interest recognition methods have been proposed: one is based on item's category similarity,the other is based on frequency for visited item's category.Additionally,advantages and disadvantages of these methods are analyzed in detail.The experimental results show that the proposed algorithm with two user's long interest recognition methods outperforms other item-based collaborative filtering algorithms.