EMPLOYING ROUGH SET THEORY TO ALLEVIATE THE SPARSITY ISSUE IN RECOMMENDER SYSTEM

摘要：

Recommender systems represent personalized services that aim at predicting a users interest on information items available in the application domain, using users1 ratings on items. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users ratings is the major reason causing the poor quality. The popular same value and singular value decomposition techniques arc 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 users 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.

关键词： Recommender system Collaborative filtering Rough set theory Sparsity

作者: CHONG-BEN HUANG SONG-JIE GONG

作者单位: Zhejiang Business Technology Institute, Ningbo 315012, P.R.China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1610-1614

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）