A Hybrid Approach to Collaborative Filtering For Overcoming Data Sparsity

摘要：

Collaborative filtering has two methodologies: user based one and item based one.The former uses the similarity between users to predict,while the latter uses the similarity between items.Although both of them are successfully applied in wide regions,they suffer from a fundamental problem: data sparsity.In this paper,we propose a hybrid approach to overcome the problem.We define a similarity weight to dealing with the data sparsity.Experimental results showed that our new approach can significantly improve the prediction accuracy of collaborative filtering.

作者: Zhang Liang Xiao Bo Guo Jun

作者单位: School of Information Engineering Beijing University of Posts and Telecommunications

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）