Research of Collaborative Filtering Recommendation Algorithm for Short Text  

作　　者：Chunxu Chao Shouning Qu Tao Du 

机构地区：[1]School of Information Science and Engineering, University of Jinan, Jinan, China [2]The Center of Information Network, University of Jinan, Jinan, China

出　　处：《Journal of Computer and Communications》2014年第14期59-66,共8页电脑和通信（英文）

摘　　要：Short text, based on the platform of web2.0, gained rapid development in a relatively short time. Recommendation systems analyzing user’s interest by short texts becomes more and more important. Collaborative filtering is one of the most promising recommendation technologies. However, the existing collaborative filtering methods don’t consider the drifting of user’s interest. This often leads to a big difference between the result of recommendation and user’s real demands. In this paper, according to the traditional collaborative filtering algorithm, a new personalized recommendation algorithm is proposed. It traced user’s interest by using Ebbinghaus Forgetting Curve. Some experiments have been done. The results demonstrated that the new algorithm could indeed make a contribution to getting rid of user’s overdue interests and discovering their real-time interests for more accurate recommendation.Short text, based on the platform of web2.0, gained rapid development in a relatively short time. Recommendation systems analyzing user’s interest by short texts becomes more and more important. Collaborative filtering is one of the most promising recommendation technologies. However, the existing collaborative filtering methods don’t consider the drifting of user’s interest. This often leads to a big difference between the result of recommendation and user’s real demands. In this paper, according to the traditional collaborative filtering algorithm, a new personalized recommendation algorithm is proposed. It traced user’s interest by using Ebbinghaus Forgetting Curve. Some experiments have been done. The results demonstrated that the new algorithm could indeed make a contribution to getting rid of user’s overdue interests and discovering their real-time interests for more accurate recommendation.

关 键 词：SHORT TEXT PERSONALIZED RECOMMENDATION Time WEIGHT FUNCTION 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]