Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Mining correlation between locations using human location history
Mining interesting locations and travel sequences from GPS trajectories
iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
Efficient Mining of Regional Movement Patterns in Semantic Trajectories
Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs
Addressing the cold-start problem in location recommendation using geo-social correlations
Understanding urban human activity and mobility patterns using large-scale location-based data from online social media
Mining Semantic Trajectory Patterns from Geo-Tagged Data
A Survey of Location Prediction on Twitter
Device-free indoor localization and tracking through Human-Object Interactions
Hierarchical geographical modeling of user locations from social media posts
LifeMap: A Smartphone-Based Context Provider for Location-Based Services
A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data
GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory
identifying important places in people ' s lives from cellular network data .
Exploiting Text and Network Context for Geolocation of Social Media Users
A clustering-based approach for discovering interesting places in trajectories
Where's @wally?: a classification approach to geolocating users based on their social ties
Location recommendation in location-based social networks using user check-in data
What's Your Next Move: User Activity Prediction in Location-based Social Networks.
Lost in Space: Geolocation in Event Data
Sensing of Salivary Glucose Using Nano-Structured Biosensors
DIBL in short-channel strained-Si n-MOSFET
A tutorial on hidden Markov models and selected applications in speech recognition
Connecting the dots : small cells shape up for high-performance indoor radio In
A Piecewise Aggregate pattern representation approach for anomaly detection in time series