1. Understanding and Organizing User Generated Data Methods and Applications

2. August 16, 1977June 25, 2009 2

3. August 16, 1977 3

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5. June 25, 2009 5

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7. officially pronounced dead 7

8. Media Social 8

9. Part 2: Similarity Part 1: Direct Links 9 This talk: Results that are directly applicable in end-user services This talk: Results that are directly applicable in end-user services

10. Part 1: Direct Links 10

11. Probability that two of my friends are (becoming) friends themselves is high! high clustering 11

12. VENETA: Friend Finding 12

13. privacy preserving! same contact = friend of a friend 13

14. Cluestr: Contact Recommendation 14

15. Clustering Survey: Communities are often addressed as groups! Survey: „There‘s no training tonight!“ „Let‘s have a BBQ tomorrow!“ „Our next meeting is at 2pm!“ 15

16. Clustering Recommend contacts from clusters of already selected contacts 16 Communities can be identified using clustering algorithm

17. recommended contacts Group (i.e. „invited“ contacts) Group updated group new recommendations Considerable time savings possible! 17

18. Part 2: Similarity 18

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20. Academic Conferences 20

21. conference publication author Similarity between Scientific Conferences 21

22. Confsearch (Screenshot) Highlight Ratings Highlight Related Conference Search 22

23. Music Similarity 23

24. How similar is Michael Jackson to Elvis Presley? 24

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26. #common users (co-occurrences) (co-occurrences) Occurrences of song A Occurrences of song B „Users who listen to Elvis also listen to...“ Problem: Only pairwise similarity, but no global view! 26

27. Getting a global view... d = ? pairwise similarities 1 1 27

28. Principal Component Analysis (PCA): – Project on hyperplane that maximizes variance. – Computed by solving an eigenvalue problem. Basic idea of MDS: – Assume that the exact positions y 1,...,y N in a high-dimensional space are given. – It can be shown that knowing only the distances d(y i, y j ) between points we can calculate the same result as applying PCA to y 1,...,y N. Problem: Complexity O(n 2 log n) – use approximation: LMDS [da Silva and Tenenbaum, 2002] Classical Multidimensional Scaling (MDS) 28

29. Problem: Some links erroneously shortcut certain paths Problem: Use embedding as estimator for distance: Remove edges that get stretched most and re-embed 29

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32. After only few skips, we know pretty well which songs match the user‘s mood After only few skips, we know pretty well which songs match the user‘s mood Realization using our map? 32

33. „In my shelf AC/DC is next to the ZZ Top...“ Browsing Covers 33

34. „from users for users“ Conclusion 34

35. Thank you 35

36. Thanks to my co-authors......and many more people! 36

37. List of Publications Social Audio Features for Advanced Music Retrieval Interfaces M. Kuhn, R. Wattenhofer, S. Welten Multimedia 2010 Visually and Acoustically Exploring the High- Dimensional Space of Music L. Bossard, M. Kuhn, R. Wattenhofer SocialCom 2009 Cluestr: Mobile Social Networking for Enhanced Group Communication R. Grob, M. Kuhn, R. Wattenhofer, M. Wirz GROUP 2009 From Web to Map: Exploring the World of Music O. Goussevskaia, M. Kuhn, M. Lorenzi, R. Wattenhofer WI 2008 VENETA: Serverless Friend-of-Friend Detection in Mobile Social Networking M. von Arb, M. Bader, M. Kuhn, R. Wattenhofer WiMob 2008 Exploring Music Collections on Mobile Devices O. Goussevskaia, M. Kuhn, R. Wattenhofer MobileHCI 2008 The Layered World of Scientific Conferences M. Kuhn and R. Wattenhofer APWeb 2008 The Theoretic Center of Computer Science M. Kuhn and R. Wattenhofer. (Invited paper) SIGACT News, December 2007 Layers and Hierarchies in Real Virtual Networks O. Goussevskaia, M. Kuhn, R. Wattenhofer WI 2007 37