AN INCREMENTAL COLLABORATIVE FILTERING ALGORITHM FOR RECOMMENDER SYSTEMS

被引：0
|
作者：
Komkhao, Maytiyanin [1 ]
Li, Zhong [1 ]
Halang, Wolfgang A. [1 ]
Lu, Jie [2 ]
机构：
[1] Fernuniv, Chair Comp Engn, D-58084 Hagen, Germany
[2] Univ Technol Sydney, Sch Software, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
来源：
UNCERTAINTY MODELING IN KNOWLEDGE ENGINEERING AND DECISION MAKING | 2012年 / 7卷
基金：
澳大利亚研究理事会;
关键词：
NETWORK;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recommender systems are effective approaches to implement personalised e-services. In recent years, they have gained widespread applications in e-commerce. Current recommender systems still need, however, further improvements with respect to the accuracy of prediction and to solve the scalability problem. To this end, an incremental collaborative filtering (InCF) algorithm based on the Mahalanobis distance is presented for recommender systems. Furthermore, the Mahalanobis radial basis function with ellipsoidal shape is employed to determine the decision boundaries of clusters. Experimental results show that the algorithm proposed can lead to improved prediction accuracy and that it turns out to be scalable in recommender applications.
引用
收藏
页码：327 / 332
页数：6
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