1 research outputs found
HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering
Mobile context determination is an important step for many context aware
services such as location-based services, enterprise policy enforcement,
building or room occupancy detection for power or HVAC operation, etc.
Especially in enterprise scenarios where policies (e.g., attending a
confidential meeting only when the user is in "Location X") are defined based
on mobile context, it is paramount to verify the accuracy of the mobile
context. To this end, two stochastic models based on the theory of Hidden
Markov Models (HMMs) to obtain mobile context are proposed-personalized model
(HPContext) and collaborative filtering model (HCFContext). The former predicts
the current context using sequential history of the user's past context
observations, the latter enhances HPContext with collaborative filtering
features, which enables it to predict the current context of the primary user
based on the context observations of users related to the primary user, e.g.,
same team colleagues in company, gym friends, family members, etc. Each of the
proposed models can also be used to enhance or complement the context obtained
from sensors. Furthermore, since privacy is a concern in collaborative
filtering, a privacy-preserving method is proposed to derive HCFContext model
parameters based on the concepts of homomorphic encryption. Finally, these
models are thoroughly validated on a real-life dataset.Comment: Mobile context, collaborative filtering, privacy-preserving,
personalized model, sensors, location, prediction, hidden markov models,
google now, apple siri, cortana, alex