A reading summary of Zheng et al (2010)

1. COLLABORATIVE LOCATIONAND
ACTIVITY RECOMMENDATIONS
WITH GPS HISTORY DATA
Hyunseok Choi

2. Background
■ We have accumulated many
location data with the
popularity of location-based
services.
■ Coordinates, time stamps, and
comments
■ Transportation routine
prediction, location-based
activity recognition, location-
based social network
GPS data management system (P 1029, Zheng, et al 2010)

3. Purpose
■ Discover interesting locations given a certain activity
■ Discover possible activities given a location
■ Location recommendation / Activity recommendation

4. Model
Location-activity matrix (entry: frequency)
(P 1030, Zheng, et al 2010)

5. Data Input
Location-activity matrix (entry: frequency)
(P 1030, Zheng, et al 2010)
http://www.mobilegpsonline.com/
Raw GPS Data
Comments

6. Data Input
Location-activity matrix (entry: frequency)
(P 1030, Zheng, et al 2010)
http://www.mobilegpsonline.com/
Raw GPS Data
Comments
Sparse Data
Over 2.5 years, we have 12,765 GPS trajectories, but
only 530 comments from 162 users

7. Architecture
(P 1031, Zheng, et al 2010)

8. Stay region extraction
Stay point s: a geographical region where a user stayed over a
time thresholdTr within a distance threshold of Dr.A virtual
location characterized by a set of consecutive GPS points.
A user’s trajectory Traj: a sequence of time-stamped points
Stay region r:A geographic region which contains a set of stay points.
Interpreted as ‘location’
(P 1030, Zheng, et al 2010)

9. ■ Grid based clustering
– Consider the size of the clusters
– d2 is the maximum size of a location cluster
– A unassigned g  + 8 neighbor grids
■ K-means algorithm
– Needs to be given k (number of clusters) values
– Doesn’t control the size of clusters
Stay region extraction
s
s s
s
s s
d/3
A stay region r
s: stay points

10. K-means Clustering (k=4)
https://commons.wikimedia.org/wiki/File:Kmeans_animation.gif

11. Thank you