Personalized%20Folksonomies%20Based%20on%20Hierarchical%20Tag%20Clustering - PowerPoint PPT Presentation

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Personalized%20Folksonomies%20Based%20on%20Hierarchical%20Tag%20Clustering

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Title: Personalized%20Folksonomies%20Based%20on%20Hierarchical%20Tag%20Clustering


1
Personalized Folksonomies Based on Hierarchical
Tag Clustering
A. Shepitsen, J. Gemmell, B. Mobasher and R.
Burke
2
Agenda
  • Overview of collaborative tagging systems
  • Search and navigation in Folksonomies
  • Personalized navigation in Folksonomies
  • Hierarchical agglomerative tag clustering
  • Experimental results
  • Conclusions

3
Storing the Resources Locally
Resource
Tag
4
Tagging the Resource on Social Tagging System
Resource
Tag
5
Delicious User Profile
User
Resources
Tags
6
Last.Fm User Profile
7
Navigation in Folksonomies
8
Search in Folksonomies
Search Tag
Resources
9
Tag Redundancy
C_sharp
C
Italian_Food
Italian_cuisine
DePaul_University
DePaul
10
Tag Ambiguity
Java
Run
11
Advantages of Personalization Based on Clustering
- Electiveness in treating tag redundancy
Eclipse
Sun
Java/JSEE
_Java
Java5
Java
_Java
Enigm
C
Java
Java/JSEE
String
JSP
Java5
12
Advantages of PersonalizationBased on Clustering
- Electiveness in treating Tag ambiguity
Ambiguous Tag Apple (Flikr social tagging system)
13
Personalization with clusters
Java
Coffee Drinker
Java
Coffee
StarBucks
Nestle
Nascafe
Morning_drink
Programmer
Java
Eclips
C
JSEE
C
Traveler
Java
West_Malaizija
Rest_Bruney
Indonesia_tours
Australia
14
Tag Similarities Measurement
R1 R2 R3
Rn
Tag1 Tag2 Tag3 Tagn
5 7 14 15
11 6 12 8
7 9 7 14
8 12 5 10
15
Similarity in non-descriptive tags/IDF
Log(N/n)
Cool
R1 R2
Rn
67 27 119
3.11 1.12 3.67
Log(N/n)
Antropology
R1 R2
Rn
29 12 23
4.35 2.7 3.78
16
Agglomerative Example (Step and matrix)
Search Tag Design
.25
.4
.55
.7
.85
1.0
Step .15
Java
java
j2ee
Web
Deals
Tools
Search
Google
Design
SearchEng
webDesign
Shopping
Coffee
Bargins
Espresso
Food
Programming
17
Cluster Cohesiveness
Single Linkage
Cluster B
Cluster A
Maximal Linkage
Cluster B
Cluster A
18
Cluster Cohesiveness
Average Centroid Linkage
Cluster B
Cluster A
19
Generalization Coefficient
Search Tag Java Generalization coefficient
2
.25
.4
.55
.7
.85
1.0
Step .15
java
Java
j2ee
Web
Deals
Tools
Search
Google
Design
SearchEng
webDesign
Shopping
Coffee
Bargins
Espresso
Food
Programming
20
Division Coefficient
Division Coefficient0.6
.25
.4
.55
.7
.85
1.0
Step .15
java
Java
j2ee
Web
Deals
Tools
Search
Design
Google
SearchEng
webDesign
Shopping
Coffee
Bargins
Espresso
Food
Programming
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Cluster6
21
Personalization
Java
Coffee
Programmimg
1
StarBucks
Nestle
URL
Nascafe
Morning_drink
Programmimg
2
URL
Java
Coffee
Eclips
C
3
URL
JSEE
C
Tourism
4
URL
Java
West_Malaizija
Rest_Bruney
Coffee
5
URL
Indonesia_tours
Australia
22
Leave one out approach
Query Tag
User
Tag Resourse
T1 T2 T3 T4 T5
R1 R2 R3 R4 R5
Target Resource
23
Personalization Explanation
Coffee Drinker
Java
Coffee
StarBucks
Nestle
Morning_drink
Nascafe
Programmer
Java
Eclips
C
C
JSEE
Java
Traveler
Java
Rest_Bruney
West_Malaizija
Indonesia_tours
Australia
24
Delicious Dataset
25
Step Coefficient chart
26
Generalization Coefficient Chart
27
Division coefficient chart
28
Maximal Complete Link
Cluster1
Tools
Google Tools SearchEng
Google
SearchEng
Espresso
Web
Food
Cluster2
Java
Tools Web Java
Cluster3
Espresso Food
29
Maximal Complete Link Clustering
30
K-Means ClusteringK
31
Comparison of Clustering techniques
32
Conclusions Future Work
Conclusions
  • Clustering is an effective means for overcoming
    tag ambiguity and tag redundancy
  • Hierarchical agglomerative clustering is found to
    be the most effective clustering technique
  • Clustering can be used effectively for other
    purposes in Folksonomies such as recommending
    tags, resources and users

Future Work
  • using PLSA and PCA to find the connection
    between users and resources
  • using clusters for recommendation purposes
  • implementing the notion of authority of users,
    tags
  • and resources in Folksonomies

33
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