1. Finding Wormholes with Flickr Geotags Maarten Clements Marcel Reinders Arjen de Vries Pavel Serdyukov December 3 rd, 2009 GIS

2. 03/12/20092 Maarten Clements PhD: personalized retrieval in Social Media Faculty of EEMCS – ICT group. Supervisors º Marcel Reinders – Prof. Bioinformatics (and more) º Arjen de Vries – CWI, Prof. MM Dataspaces

3. 03/12/20093 Maarten Clements Location prediction Predict relevant locations º Location  Location º User  Location Why? Flickr: MarsWFlickr: msokal 1 2 ?

4. 03/12/20094 Maarten Clements Location prediction

5. 03/12/20095 Maarten Clements Flickr Foto sharing website º Billions of photos º Active community: º Tags, Geotags, Favorites, Comments… 2009 2008 32.3M 91.4M Geotags in flickr

6. 03/12/20096 Maarten Clements Flickr Using Flickr API to collect data: º http://www.flickr.com/services/api/ http://www.flickr.com/services/api/ Strategy to find people who geotag: First collected top cities in 2008 1. 'New York, NY, United States' 2. 'London, England, United Kingdom' 3. 'San Francisco, California, United States' 4. 'Paris, Ile-de-France, France' 5. … 8643. Lo Verdes, Canary Islands, Spain

7. 03/12/20097 Maarten Clements Flickr Repeat: º Select a city based on full distribution º Get a photo at this location (geotagged) º Select the user who made the photo º Get all this users photos City

8. 03/12/20098 Maarten Clements Flickr Users:36,264 Photos: 52,425,279 Geo Tags: 22,710,496

9. 03/12/20099 Maarten Clements Flickr Tags Titles Time stamps Social network Descriptions Groups

10. 03/12/200910 Maarten Clements Flickr

11. 03/12/200911 Maarten Clements Wormholes Places that are similar but not necessarily spatially close. Use user travel patterns to detect these places Assumptions º Users have a certain travel preference º Users make photos at places they like

12. 03/12/200912 Maarten Clements Wormholes Given a target location, find relevant users Weigh Euclidean distance with normal distribution

13. 03/12/200913 Maarten Clements Wormholes Given a target location, find relevant users Weigh Euclidean distance with normal distribution Aggregate data over all users, using computed weights º 2000x4000 histogram, example 4x8: User 1:User 2:User 1+2:

14. 03/12/200914 Maarten Clements Convolution: Wormholes Given a target location, find relevant users Weigh Euclidean distance with normal distribution Aggregate data over all users, using computed weights Compute convolution with Gaussian kernel Compute difference with expected geotag distribution

15. 03/12/200915 Maarten Clements Wormholes Result

16. 03/12/200916 Maarten Clements Wormholes Sigma determines how many users we call Relevant σ σ Many relevant usersFew relevant users

17. 03/12/200917 Maarten Clements Evaluation Find ground truth data: Wikipedia, GeoNames

18. 03/12/200918 Maarten Clements Evaluation Rank predicted peaks and compute precision Is there a mountain in a range of 3cells around the predicted peak? 0102030405060708090100 0 0.05 0.1 0.15 0.2  Average Precision σ (km) So… Does it work?

19. 03/12/200919 Maarten Clements Evaluation (manual)

20. 03/12/200920 Maarten Clements Evaluation (manual) σ = 100km

21. 03/12/200921 Maarten Clements Evaluation (manual) σ = 20m Target: Tour Eiffel

22. 03/12/200922 Maarten Clements Evaluation (manual) σ = 20m Target: Tour Eiffel

23. 03/12/200923 Maarten Clements Evaluation (manual) σ = 80m Target: Tour Eiffel

24. 03/12/200924 Maarten Clements Evaluation (manual) σ = 80m Target: Tour Eiffel

25. 03/12/200925 Maarten Clements Evaluation (manual) Target: Tour Eiffel σ = 300m

26. 03/12/200926 Maarten Clements Evaluation (manual) Target: Tour Eiffel σ = 300m

27. 03/12/200927 Maarten Clements Evaluation (manual) σ = 60m Target: Pere Lachaise

28. 03/12/200928 Maarten Clements Evaluation (manual) σ = 60m Target: Pere Lachaise

29. 03/12/200929 Maarten Clements What next? User  Location Query exists of multiple points (instead of 1) Get rid of grid based prediction º Compute kernel convolution peaks directly from continuous geotag data.

30. 03/12/200930 Maarten Clements What next?

31. 03/12/200931 Maarten Clements What next?

32. 03/12/200932 Maarten Clements Conclusions We have proposed a new method to predict similar locations based on geotags. Scale parameter can be used to predict relevant locations at different scales. ECIR’10: Comparing different user aggregation methods

33. 03/12/200933 Maarten Clements http://ict.ewi.tudelft.nl/~maarten/wormholes/ M.Clements@tudelft.nl http://ict.ewi.tudelft.nl/~maarten/wormholes/ M.Clements@tudelft.nl