IMPLEMENT OF ITEM-BASED RECOMMENDATION ON GPU

摘要：

　　Recommemder System is becoming more and more important for getting information in recent 20 years.But recommender system has the weakness of extreed large scale that makes it delayable for recommendation,which making it cannot offer real-time service.Business recommender system is general divided into two parts,the on-line recommend part and the off-line calculation part.It precomputes the off-line part to get quicker recommendation when needed.Pretended real-time recommendation is a compromise with the growing and changing system.We propose the better way to get better real-time service by processing the off-line calculation on GPU,which is a high-speed parallel processor,to speed up the first part of recommender system to get more real-time service.Our experiments show,the off-line part can speed up 19 times when using GPU,and the larger of the data scale,the better it can improve.

关键词： Recommender system Off-line calculation GPU Efficiency

作者: Zhanchun Gao Yuying Liang Yanjun Jiang

作者单位: School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 764-767

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）