An accurate and scalable collaborative recommender

被引：13

作者：

Kelleher, J ^{[1
]}

Bridge, D ^{[1
]}

机构：

[1] Natl Univ Ireland Univ Coll Cork, Dept Comp Sci, Cork, Ireland

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2004年 / 21卷 / 3-4期

关键词：

clustering; collaborative filtering; recommender systems;

D O I：

10.1023/B:AIRE.0000036255.53433.26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a collaborative recommender that uses a user-based model to predict user ratings for specified items. The model comprises summary rating information derived from a hierarchical clustering of the users. We compare our algorithm with several others. We show that its accuracy is good and its coverage is maximal. We also show that the algorithm is very efficient: predictions can be made in time that grows independently of the number of ratings and items and only logarithmically in the number of users.

引用

页码：193 / 213

页数：21

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