Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MPRV.2010.56 | IEEE Pervasive Computing |
Keywords | Field | DocType |
Collaboration,Recommender systems,IP networks,Scattering | Space time,Recommender system,World Wide Web,Collaborative software,Computer science,Internet of Things,Ubiquitous computing,Multimedia,The Internet | Journal |
Volume | Issue | ISSN |
9 | 3 | 1536-1268 |
Citations | PageRank | References |
18 | 0.85 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario Munoz-Organero | 1 | 73 | 11.70 |
| Gustavo A. Ramíez-González | 2 | 18 | 0.85 |
| Pedro J. Munoz-Merino | 3 | 25 | 2.83 |
| Carlos Delgado Kloos | 4 | 1121 | 172.07 |