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
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
收藏
页码:81 / 87
页数:7
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