Understanding and Organizing User Generated Data Methods and Applications

August 16, 1977 June 25, 2009

August 16, 1977

June 25, 2009

officially pronounced dead

Media Social In 1977, only few people had the possibility to address others by means of Radio and TV. They broadcasted to the public whatever they felt was interesting. Nowadays things look different. People are networked and got directly involved in the information flow. Similarly, media items are interconnected, and they are both, produced and cosumed by people. A significant amount of the information flow nowadays occurs over the internet, using computers, and, more and more, mobile devices. The result are novel data structures that demand being investigated. In fact, this is the context this thesis lives in. We try to investigate and exploit the data and structures that have emerged from the use of social media.

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

Part 1: Direct Links

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

VENETA: Friend Finding

I want to meet people! same contact = friend of a friend privacy preserving!

Cluestr: Contact Recommendation

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

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

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

Part 2: Similarity

Academic Conferences

Similarity between Scientific Conferences publication conference author

Confsearch (Screenshot) Highlight Ratings Highlight Related Conference Search

Music Similarity

How similar is Michael Jackson to Elvis Presley?

Audio Analysis Usage Data

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

1 2 3 Getting a global view... d = ? pairwise similarities graph for all-pairs distances (shortest path) 2 MDS to embed graph into Euclidean space while approximately preserving distances (=> global view) 3

Classical Multidimensional Scaling (MDS) 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 y1,...,yN in a high-dimensional space are given. It can be shown that knowing only the distances d(yi, yj) between points we can calculate the same result as applying PCA to y1,...,yN. Problem: Complexity O(n2 log n) use approximation: LMDS [da Silva and Tenenbaum, 2002]

estimator for distance: Remove edges that get Use embedding as estimator for distance: Remove edges that get stretched most and re-embed Problem: Some links erroneously shortcut certain paths Example: Kleinberg graph (20x20 grid with random edges) Original embedding (spring embedder) After 6 rounds After 12 rounds After 30 rounds Kleinberg graph: grid edges + one random edge per node (follows 1/d^2 distribution, d=manhattan distance)

Play „random“ songs that match my mood! skip = listen =

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

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

Conclusion „from users for users“

Thank you

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 WI 2007