Adapting ratings in memory-based collaborative filtering using linear regression

被引:1
|
作者
Kunegis, Jerome [1 ]
Albayrak, Ahin [1 ]
机构
[1] Tech Univ Berlin, DAI Labor, Ernst Reuter Platz 7, D-10587 Berlin, Germany
关键词
D O I
10.1109/IRI.2007.4296596
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We show that the standard memory-based collaborative filtering rating prediction algorithm using the Pearson correlation can be improved by adapting user ratings using linear regression. We compare several variants of the memory-based prediction algorithm with and without adapting the ratings. We show that in two well-known publicly available rating datasets, the mean absolute error and the root mean squared error are reduced by as much as 20% in all variants of the algorithm tested.
引用
收藏
页码:49 / +
页数:2
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