Similarity of mobile users based on sparse location...
Transcript of Similarity of mobile users based on sparse location...
![Page 1: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/1.jpg)
Similarity of mobile usersbased on sparse location history
Pasi FräntiRadu Mariescu-Istodor
Karol Waga
21.2.2019
P. Fränti, R. Mariescu-Istodor and K. Waga
”Similarity of mobile users based on sparse location history”
Int. Conf. Artificial Intelligence and Soft Computing (ICAISC)
Zakopane, Poland, 593-603, June 2018
![Page 2: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/2.jpg)
Introduction
![Page 3: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/3.jpg)
Motivation
•
Cold start users in Recommender systems
•
Activity in the same area •
Opinions of local experts more valuable [Bao’2012]
•
Identify the same user
![Page 4: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/4.jpg)
Existing methods
•
Complete trajectories [Ying’10]•
Longest common subsequence with speed and turn points [Liu’12]
•
Stop points (>30 min) [Zheng’10]
•
Most common stops: home and work [Biagioni’13]
•
User given priority for parts more [Wang’12]
![Page 5: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/5.jpg)
Problems?
•
Full GPS trajectories not always stored
•
Areas of activity are often more important than the exact tracks
•
Explicit activities such as visits, check-ins and photo taking are widely recorded.
![Page 6: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/6.jpg)
Test case: APR trio
Andrei 201214
412
8
4
5
3
4
20
7
9 20
5
3
Andrei 2013
46
11
6
8
3
Andrei 2014
Pasi 2012
10
12
Pasi 2013
8
5
8
5
Pasi 2014
9
9
Radu 2012
18
28
Radu 2013
13
11
16
Radu 2014
6
21
8
11
![Page 7: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/7.jpg)
A11
A13
A12
A14
46
11
6
8
3
20
7
9 20
5
3
14
412
8
4
5
3
410
6
6
11
Example of User A
![Page 8: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/8.jpg)
Pasi 2011
Pasi 2013
Pasi 2012
Pasi 2014
5
610
12
8
4
5
8
59
9
Example of User P
![Page 9: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/9.jpg)
Radu 2011
Radu 2013
Radu 2012
Radu 2014
19
32
18
28
13
11 6
16 21
8
11
Example of User R
![Page 10: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/10.jpg)
Two approaches
1. Histogram comparison-
Construct histogram from some common data
-
Map points to the bins-
Histogram matching of the two counts
2. Clustering comparison-
Cluster each data separately
-
Map centroids between the data-
Calculate similarity based on the mapping
![Page 11: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/11.jpg)
Selected approaches
1. Histogram comparison-
Construct histogram from some common data
-
Map points to the bins-
Histogram matching of the two counts
2. Clustering comparison-
Cluster each data separately
-
Map centroids between the data-
Calculate similarity based on the mapping
![Page 12: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/12.jpg)
Approach 1:Histograms
![Page 13: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/13.jpg)
Location historyActivity points of users
![Page 14: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/14.jpg)
Histogram matchingCreate bins from popular locations
![Page 15: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/15.jpg)
Location similarityCount visit statistics
1 0 0
1 0 0
0 0 0
0 0 3
0 0 2
2 2 0
1 1 1
4 3 19
76
Histogrambins
Visitcounts
![Page 16: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/16.jpg)
Histogram of the toy example
4 3
1
2 2
0
0 0
3
1 1
1
0 0
2
1 0
0
1 0
0
0 0
0
9 6 7
8
43
3
2
1
1
0
0.44 0.50
0.14
0.22 0.33
0.00
0.00 0.00
0.43
0.11 0.17
0.14
0.00 0.00
0.28
0.11 0.00
0.00
0.11 0.00
0.00
0.00 0.00
0.00
Pekka Bartek Julia Pekka Bartek JuliaFrequencies Relative frequencies
![Page 17: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/17.jpg)
Distance measures
![Page 18: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/18.jpg)
Bhattacharyya distance
4 3
1
2 2
0
0 0
3
1 1
1
0 0
2
1 0
0
1 0
0
0 0
0
9 6 7
8
43
3
2
1
1
0
0.44 0.50
0.14
0.22 0.33
0.00
0.00 0.00
0.43
0.11 0.17
0.14
0.00 0.00
0.28
0.11 0.00
0.00
0.11 0.00
0.00
0.00 0.00
0.00
ii qpD ln
0.47
0.27
0.00
0.14
0.00
0.00
0.00
0.00
= 0.88
-ln = 0.13
0.26
0.00
0.00
0.15
0.00
0.00
0.00
0.000.410.89
Pekka Bartek Julia Pekka Bartek JuliaFrequencies Relative frequencies
Pekkavs
Bartek
Bartekvs
Julia
![Page 19: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/19.jpg)
L1 distance
4 3
1
2 2
0
0 0
3
1 1
1
0 0
2
1 0
0
1 0
0
0 0
0
9 6 7
8
43
3
2
1
1
0
0.44 0.50
0.14
0.22 0.33
0.00
0.00 0.00
0.43
0.11 0.17
0.14
0.00 0.00
0.28
0.11 0.00
0.00
0.11 0.00
0.00
0.00 0.00
0.00
0.06
0.11
0.00
0.06
0.00
0.11
0.11
0.00
= 0.42
0.36
0.33
0.43
0.03
0.28
0.00
0.00
0.001.43
i
ii qpL1
![Page 20: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/20.jpg)
4 3
1
2 2
0
0 0
3
1 1
1
0 0
2
1 0
0
1 0
0
0 0
0
9 6 7
8
43
3
2
1
1
0
0.44 0.50
0.14
0.22 0.33
0.00
0.00 0.00
0.43
0.11 0.17
0.14
0.00 0.00
0.28
0.11 0.00
0.00
0.11 0.00
0.00
0.00 0.00
0.00
0.36%
1.21%
0.00%
0.36%
0.00%
1.21%
1.21%
0.00%
= 0.04
13%
11%
18%
0%
8%
0%
0%
0%0.50
i
ii qpL 22
L2 distance
![Page 21: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/21.jpg)
•
Uniform global grid (e.g. 2525 m)•
Some known places (Mopsi services)
•
All user data from Mopsi•
The two data as such:-
Cluster the joint activity data
-
Each cluster represents one bin-
Count blues and reds in the cluster using any histogram matching technique
How to create histogram bins
![Page 22: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/22.jpg)
Approach 2:Clustering
![Page 23: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/23.jpg)
Clustering of two distributions
6
10
4
1 7/1 = 7
9/6 = 1.5
4/0.5 = 8
6/0.5 = 12
![Page 24: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/24.jpg)
![Page 25: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/25.jpg)
Experimental results
![Page 26: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/26.jpg)
Collected data
Municipalities:Municipalities:JoensuuJoensuuLiperiLiperiOutokumpuOutokumpuPolvijPolvijäärvirviKontiolahtiKontiolahtiIlomantsiIlomantsiJuukaJuuka
63.44N28.65E
62.25N31.58E
Joensuu sub-region bounding box
•
Histogram from 293 places (Mopsi services)•
User activities until 31.12.2014‒
Photos taken
‒
Tracking started or ended
![Page 27: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/27.jpg)
Summary of APR trio data 2011-2014
Distinct users:
3Total users:
12
Sample sizes:
37 -
1263
14
412
8
4
5
3
4
Andrei2012
Andrei2011
10
6
6
11
Andrei2011
10
6
6
11
20
7
9 20
5
3
Andrei2013
20
7
9 20
5
3
Andrei2013
46
11
6
8
3
Andrei201446
11
6
8
3
Andrei2014
19
32Radu201119
32Radu2011
18
28
10
Radu201218
28
10
Radu2012
13
11
16 Radu2013
13
11
16 Radu2013
6
21
8
11
9
Radu20146
21
8
11
9
Radu2014
5
64
Pasi2011 5
64
Pasi2011
10
12Pasi201210
12Pasi2012
9
9
Pasi20149
9
Pasi2014
8
5
8
5
Pasi20138
5
8
5
Pasi2013
![Page 28: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/28.jpg)
Ten most popular histogram bins Frequencies shown
Work
Home
Home
![Page 29: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/29.jpg)
Test protocolExpected result
True positive:
13
11
16 Radu2013
13
11
16 Radu2013 5
64
Pasi2011 5
64
Pasi2011
True negative:
vs.9
9
Pasi20149
9
Pasi20145
64
Pasi2011 5
64
Pasi2011 vs.
Distance Distance
SAME DIFFERENT
• 12*12 = 144 comparisons in total• 3*4*4 = 48 true positives (33%)• 3*8*8 = 96 true negatives (67%)
![Page 30: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/30.jpg)
??
Comparison of all pairs
vs.
Distance
0.01 0.01 0.02 0.03 0.05 0.06 …
0.74 0.76 0.81 0.82 0.88
Same user
threshold
Different user
Sort
Algorithm result:
![Page 31: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/31.jpg)
Classification error
0.01 0.01 0.02 0.03 0.05 0.06 …
0.74 0.76 0.81 0.82 0.88
Same user Different user
same same not same same same …
not not not not not
Algorithm result:
Ground truth:
Errors made:
3Total comparisons:
144
Classification error:
2%
![Page 32: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/32.jpg)
Classification results
Averagedistance A priori
Top-33% Best possible
Fuzzy worse than crisp
Error Error
![Page 33: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/33.jpg)
What if top-10 places removed?
![Page 34: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/34.jpg)
Conclusions
Main results:•
People’s identity can be recognized with high accuracy
•
All except L2
and L•
Fuzzy works worse than crisp
Future research:•
Clustering-based approach
•
K-NN classifier
![Page 35: Similarity of mobile users based on sparse location historycs.joensuu.fi/sipu/pub/LocationSimilarity.pdf · based on sparse location history. Pasi Fränti. Radu Mariescu-Istodor.](https://reader036.fdocuments.in/reader036/viewer/2022071102/5fdc13fea6a9310acd6cabfa/html5/thumbnails/35.jpg)
Thank you
Time for Time for questions!questions!