Employing rough set theory to alleviate the sparsity issue in recommender system - Chong-Ben Huang; Song-Jie Gong (2008 International Conference on Machine Learning and Cybernetics, 2008)
Recommender systems represent personalized services that aim at predicting a userpsilas interest on information items available in the application domain, using userspsila ratings on items. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of userspsila ratings is the major reason causing the poor quality. The popular same value and singular value decomposition techniques are able to alleviate this issue. But they also introduce new problems. A collaborative filtering based on rough set theory was proposed to solve this problem, which predicts values of the null ratings in the candidates, and gets the results using userpsilas neighbors. Experimental results show that this method can increase the accuracy of the predicted values, resulting in improving recommendation quality of the collaborative filtering recommender system.