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» Using Temporal Data for Making Recommendations
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CCS
2008
ACM
14 years 11 months ago
User-controllable learning of security and privacy policies
Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have succ...
Patrick Gage Kelley, Paul Hankes Drielsma, Norman ...
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
15 years 10 months ago
Mobile call graphs: beyond power-law and lognormal distributions
We analyze a massive social network, gathered from the records of a large mobile phone operator, with more than a million users and tens of millions of calls. We examine the distr...
Mukund Seshadri, Sridhar Machiraju, Ashwin Sridhar...
CMOT
2010
203views more  CMOT 2010»
14 years 6 months ago
A Bayesian approach to modeling lost person behaviors based on terrain features in Wilderness Search and Rescue
: In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important be...
Lanny Lin, Michael A. Goodrich
CIKM
2009
Springer
15 years 4 months ago
Post-rank reordering: resolving preference misalignments between search engines and end users
No search engine is perfect. A typical type of imperfection is the preference misalignment between search engines and end users, e.g., from time to time, web users skip higherrank...
Chao Liu, Mei Li, Yi-Min Wang
CONEXT
2009
ACM
14 years 10 months ago
StarClique: guaranteeing user privacy in social networks against intersection attacks
Building on the popularity of online social networks (OSNs) such as Facebook, social content-sharing applications allow users to form communities around shared interests. Millions...
Krishna P. N. Puttaswamy, Alessandra Sala, Ben Y. ...