understanding and organizing user generated data methods and applications
Post on 21-Dec-2015
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TRANSCRIPT
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Part 2: Similarity
Part 1: Direct LinksThis talk:
Results that are directly applicable in end-user services
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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!“
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ClusteringRecommend contacts from clusters of already
selected contacts
Communities can be identified using
clustering algorithm
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recommended contacts
Group
(i.e. „invited“ contacts)
updated group
new recommendations
Considerable time savings possible!
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ba
cBAsim
),(
#common users (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!
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Getting a global view...
d = ?
pairwise similarities1
graph for all-pairsdistances (shortest path)
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MDS to embed graph into Euclidean spacewhile approximately preserving distances (=> global view)
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• 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]
Classical Multidimensional Scaling (MDS)
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Example: Kleinberg graph (20x20 grid with random edges)
Original embedding(spring embedder) After 6 rounds After 12 rounds After 30 rounds
Problem:
Some links erroneously shortcut certain paths
Use embedding as estimator for distance:
Remove edges that get
stretched most and re-embed
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After only few skips, we know pretty well which songs match the user‘s mood
Realization using our map?
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List of PublicationsSocial Audio Features for Advanced Music Retrieval InterfacesM. Kuhn, R. Wattenhofer, S. WeltenMultimedia 2010
Visually and Acoustically Exploring the High-Dimensional Space of MusicL. Bossard, M. Kuhn, R. WattenhoferSocialCom 2009
Cluestr: Mobile Social Networking for Enhanced Group CommunicationR. Grob, M. Kuhn, R. Wattenhofer, M. WirzGROUP 2009
From Web to Map: Exploring the World of MusicO. Goussevskaia, M. Kuhn, M. Lorenzi, R. WattenhoferWI 2008
VENETA: Serverless Friend-of-Friend Detection in Mobile Social NetworkingM. von Arb, M. Bader, M. Kuhn, R. WattenhoferWiMob 2008
Exploring Music Collections on Mobile DevicesO. Goussevskaia, M. Kuhn, R. WattenhoferMobileHCI 2008
The Layered World of Scientific ConferencesM. Kuhn and R. WattenhoferAPWeb 2008
The Theoretic Center of Computer ScienceM. Kuhn and R. Wattenhofer. (Invited paper)SIGACT News, December 2007
Layers and Hierarchies in Real Virtual NetworksO. Goussevskaia, M. Kuhn, R. WattenhoferWI 2007