Improving recommendation novelty based on topic taxonomy

被引：0

作者：

Weng, Li-Tung ^{[1
]}

Xu, Yue ^{[1
]}

Li, Yuefeng ^{[1
]}

Nayak, Richi ^{[1
]}

机构：

[1] Queensland Univ Technol, Brisbane, Qld, Australia

来源：

PROCEEDING OF THE 2007 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WORKSHOPS | 2007年

关键词：

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：

Clustering has been a widely applied approach to improve the computation efficiency of collaborative filtering based recommendation systems. Many techniques have been suggested to discover the item-to-item, user-to-user, and item-to-user associations within user clusters. However, there are few systems utilize the cluster based topic-to-topic associations to make recommendations. This paper suggests a taxonomy-based recommender system that utilizes cluster based topic-to-topic associations to improve its recommendation quality and novelty.

引用

页码：115 / 118

页数：4