A USER SELECTION METHOD IN ADVERTISING RECOMMENDATIONS

摘要：

User recommendation problem is important for mobile operators when they provide some new service to users. The traditional methods provide a low success rate. In this paper, we present a novel user selection method of advertising recommendation according to the maximal frequent items discovery theory. The experimental results demonstrate that our method can improve the success rate dramatically and reduce the amount of garbage advertisements.

关键词： User selection Mazimum frequent itemset Classification Recommendation system

作者: Xiaoli Wu Bo Xiao Zhiqing Lin

作者单位: Pattern Recognition & Intelligent System Lab,Beijing University of Posts and Telecommunications, Beijing

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

会议地点: 北京

会议语种:英文

页码: 410-413

在线出版日期: 2009-11-06（万方平台首次上网日期，不代表论文的发表时间）