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Collaborative Location and Activity Recommendations with GPS History Data. Vincent W. Zheng † , Yu Zheng ‡ , Xing Xie ‡ , Qiang Yang † † Hong Kong University of Science and Technology ‡ Microsoft Research Asia.
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Vincent W. Zheng†, Yu Zheng‡, Xing Xie‡, Qiang Yang†
†Hong Kong University of Science and Technology
‡Microsoft Research Asia
This work was done when Vincent was doing internship in Microsoft Research Asia.
Some places are more
popular than the others
“The food is delicious”
--> dining at that place
Q1: what can I do there if I visit some place?
(Activity recommendation given location query)
An entry denotes how
popular an activity is
performed at a location
Ranking along the
Columns or rows
Forbidden City > Bird’s Nest > Zhongguancun
Tourism > Exhibition > Shopping
stay region r
Raw GPS points
* In GPS logs, we have some user comments associated with the trajectories. Shown later.
[K = 200]
[ε=0.001, MinPts = 4]
[ε=0.05, MinPts = 4]
(4) Grid clustering
GPS: “39.903, 116.391, 14/9/2009 15:25”
Stay Region: “39.910, 116.400 (Forbidden City)”
“We took a tour bus to see around
along the forbidden city moat …”
User comments are few -> this matrix is sparse!
Our objective: to fill this matrix.
Stay Region: “39.980, 116.306 (Zhongguancun)”
[restaurant, bank, shop] = [3, 1, 1]
TF-IDF style normalization*: feature = [0.13, 0.32, 0.18]
TF-IDF (Term-Frequency Inverse Document Frequency):
Assume in 10 locations, 8 have restaurants (less
distinguishing), while 2 have banks and 4 have shops:
tf-idf(restaurant) = (3/5)*log(10/8) = 0.13
tf-idf(bank) = (1/5)*log(10/2) = 0.32
tf-idf(shop) = (1/5)*log(10/4) = 0.18
“Tourism and Amusement”
“Food and Drink”
Correlation = h(1.16M),
where h is a normalization func.
Most mined correlations are reasonable. Example: “Tourism” with other activities.
more likely to happen
Web search (from Bing)
Human design (average on 8 subjects)
Example: for a rating <1,3,0,2>
One-tail t-test p1<0.01, two-tail t-test p2<0.01
One-tail t-test p1<0.05, two-tail t-test p2<0.05
We are here.
over the simple baseline without exploiting any additional info
Vincent W. Zheng