Service Recommendation Method Based on Collaborative Filtering and Random Forest

被引:0
作者
Xing, Lijing [1 ]
Ma, Delong [2 ]
Ma, Bingxian [1 ]
机构
[1] Univ Jinan, Sch Informat Sci & Engn, Jinan, Peoples R China
[2] Soochow Univ, Sch Mech & Elect Engn, Suzhou, Peoples R China
来源
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MANAGEMENT, COMPUTER AND EDUCATION INFORMATIZATION | 2015年 / 25卷
关键词
Services Recommended; Collaborative Filtering; Cross Validation Model; Random Forest Model; Multiply Users;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the development and popularization of Ecommerce, more and more information services have appeared on the web. In order to meet users requirements more accurately, several service recommendation systems had been set up. Many methods have been proposed to discover users' interests for service recommendation, such as collaborative filtering and content based service recommendation. In this paper, a new service recommendation method is proposed based on user's interest, which combines collaborative filtering based on multiply users and random forest based on single user, and this fusion method uses cross validation model. This method can improve cold start and pick up speed. Experiment results show that the method can discover users' interest efficiently and is more accurate. This method can combine two basic methods so that the result is more accurate.
引用
收藏
页码:17 / 21
页数:5
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