Improvement of Collaborative Filtering algorithm based on Hesitation Degree
Xiangwei Mu, Yan Chen, Jinsong Zhang · 2010
With the fast development of World Wide Wed, Web-based applications and services should allow user to get the right personalized information quickly and effectively. Collaborative Filtering acts a very important role in web service personalization and Recommender System. In this paper, Hesitation Degree was proposed to improve the accuracy of collaboration filtering both based on item and user, kinds of Hesitation Degree were introduced into item and user similarity computation, and the results show that the prediction accuracy can be improved from 10 percents to 25 percents in different case, using this improved similarity algorithm, Mean Absolute Error can be also reduced faster than classic methods.