Recommender Systems and Linked Open Data

被引:40
作者
Di Noia, Tommaso [1 ]
Ostuni, Vito Claudio [2 ]
机构
[1] Polytech Univ Bari, SisInf Lab, I-70125 Bari, Italy
[2] Pandora Media Inc, Oakland, CA USA
来源
REASONING WEB: WEB LOGIC RULES | 2015年 / 9203卷
关键词
USER PROFILES;
D O I
10.1007/978-3-319-21768-0_4
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We present an overview on recommender systems and we sketch how to use Linked Open Data to build a new generation of semantics-aware recommendation engines.
引用
收藏
页码:88 / 113
页数:26
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