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Cooperative Communities (CoCo): Exploiting Social Networks for Large-scale Modeling of Human Behavior
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ISSN: 1536-1268
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| Nicholas Lane, Ye Xu, Hong Lu, Shane Eisenman, Tanzeem Choudhury, Andrew Campbell, "Cooperative Communities (CoCo): Exploiting Social Networks for Large-scale Modeling of Human Behavior," IEEE Pervasive Computing, vol. 99, no. 1, pp. , , 5555. | |||
| BibTex | x | ||
| @article{ 10.1109/MPRV.2011.62, author = {Nicholas Lane and Ye Xu and Hong Lu and Shane Eisenman and Tanzeem Choudhury and Andrew Campbell}, title = {Cooperative Communities (CoCo): Exploiting Social Networks for Large-scale Modeling of Human Behavior}, journal ={IEEE Pervasive Computing}, volume = {99}, number = {1}, issn = {1536-1268}, year = {5555}, doi = {http://doi.ieeecomputersociety.org/10.1109/MPRV.2011.62}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - IEEE Pervasive Computing TI - Cooperative Communities (CoCo): Exploiting Social Networks for Large-scale Modeling of Human Behavior IS - 1 SN - 1536-1268 SP EP EPD - A1 - Nicholas Lane, A1 - Ye Xu, A1 - Hong Lu, A1 - Shane Eisenman, A1 - Tanzeem Choudhury, A1 - Andrew Campbell, PY - 5555 KW - A.0 General KW - B Hardware KW - I.2 Artificial Intelligence KW - I.1.4 Applications KW - I.5 Pattern Recognition KW - I.2 Artificial Intelligence KW - G.3.h Probabilistic algorithms KW - G.3.e Markov processes KW - C.5.7 Wearable Computers KW - C.3.h Ubiquitous computing KW - C.2.2.a Applications KW - C.2.4 Distributed Systems KW - C.2.8 Mobile Computing KW - C.3.h Ubiquitous computing KW - C.3.h Ubiquitous computing KW - H.1.2.b Human-centered computing KW - H.5.2 User Interfaces KW - J.9.a Location-dependent and sensitive KW - J.9.d Pervasive computing KW - J.9 Mobile Applications KW - J.9.f Wireless sensor networks KW - J.9.e Wearable computers and body area networks KW - I.5.2.a Classifier design and evaluation KW - I.1.4 Applications KW - I.5.4.j Remote sensing KW - VL - 99 JA - IEEE Pervasive Computing ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MPRV.2011.62
Human behavior modeling at a large-scale and under real-world conditions is still an open problem. Existing classification models do not always perform well on a diverse population. Training personalized models that incorporate different contextual conditions and individual user characteristics are effective in addressing this challenge. However, this approach burdens the users with collecting and manually labeling their own training data which is not scalable. In this article, we propose CoCo (Cooperative Communities), a learning framework that leverages different types of everyday social connections between people to personalize classification models. CoCo exploits social networks to selectively combine small contributions of labeled data from people with shared context or user characteristics. Under CoCo a personalized classifier is trained for each individual user, but by exploiting social networks, the burden of providing training data can be spread over the entire community.
Index Terms:
A.0 General, B Hardware, I.2 Artificial Intelligence, I.1.4 Applications, I.5 Pattern Recognition, I.2 Artificial Intelligence, G.3.h Probabilistic algorithms, G.3.e Markov processes, C.5.7 Wearable Computers, C.3.h Ubiquitous computing, C.2.2.a Applications, C.2.4 Distributed Systems, C.2.8 Mobile Computing, C.3.h Ubiquitous computing, C.3.h Ubiquitous computing, H.1.2.b Human-centered computing, H.5.2 User Interfaces, J.9.a Location-dependent and sensitive, J.9.d Pervasive computing, J.9 Mobile Applications, J.9.f Wireless sensor networks, J.9.e Wearable computers and body area networks, I.5.2.a Classifier design and evaluation, I.1.4 Applications, I.5.4.j Remote sensing,
Citation:
Nicholas Lane, Ye Xu, Hong Lu, Shane Eisenman, Tanzeem Choudhury, Andrew Campbell, "Cooperative Communities (CoCo): Exploiting Social Networks for Large-scale Modeling of Human Behavior," IEEE Pervasive Computing, 29 Sept. 2011. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/MPRV.2011.62>
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