A Collaborative Recommender System Based on Space-Time Similarities

被引：26

作者：

Munoz-Organero, Mario ^{[1
]}

Ramirez-Gonzalez, Gustavo A. ^{[1
]}

Munoz-Merino, Pedro J. ^{[1
]}

Delgado Kloos, Carlos ^{[1
]}

机构：

[1] Univ Carlos III Madrid, E-28903 Getafe, Spain

来源：

IEEE PERVASIVE COMPUTING | 2010年 / 9卷 / 03期

关键词：

collaborative recommender systems; Internet of Things; networking and communications; NFC tagged environments; pervasive content; Recommender systems;

D O I：

10.1109/MPRV.2010.56

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Internet of Things (IoT) concept promises a world of networked and interconnected devices that provides relevant content to users. Recommender systems can find relevant content for users in IoT environments, offering a user-adapted personalized experience. Collaboration-based recommenders in IoT environments rely on user-to-object, space-time interaction patterns. This extension of that idea takes into account user location and interaction time to recommend scattered, pervasive context-embedded networked objects. The authors compare their proposed system to memory-based collaborative methods in which user similarity is based on the ratings of previously rated items. Their proof-of-concept implementation was used in a real-world scenario involving 15 students interacting with 75 objects at Carlos III University of Madrid. © 2010 IEEE.

引用

页码：81 / 87

页数：7

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