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Personalized Folksonomies Based on Hierarchical Tag Clustering

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Presentation on theme: "Personalized Folksonomies Based on Hierarchical Tag Clustering"— Presentation transcript:

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
Tag

4 Tagging the Resource on Social Tagging System

5 Delicious User Profile
Resources Tags

6 Last.Fm User Profile

7 Navigation in Folksonomies
Tags Resources WWW . Users

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 Resources WWW . Java5 Java _Java Enigm C# Java Java/JSEE String JSP Java5

12 Advantages of Personalization Based on Clustering
- Electiveness in treating Tag ambiguity Ambiguous Tag Apple (Flikr social tagging system)

13 Personalization with clusters
Java Coffee Drinker Java Coffee Programmimg URL Nestle StarBucks Morning_drink Nascafe Programmimg URL Programmer Java Eclips C# Coffee URL JSEE C++ Tourism URL Traveler Java Rest_Bruney West_Malaizija Coffee URL Indonesia_tours Australia

14 Tag Similarities Measurement
R R R Rn Tag1 Tag2 Tag3 Tagn 5 7 14 15 11 6 12 8 9 10

15 Similarity in non-descriptive tags /IDF
*Log(N/n) Cool R R ……… Rn 67 27 ……… 119 3.11 1.12 ……… 3.67 *Log(N/n) Antropology R R ……… 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 Deals j2ee java Java Web Design Google Tools Search Shopping webDesign SearchEng Food Espresso Coffee Bargins Programming

17 Cluster Cohesiveness Single Linkage Maximal Linkage Cluster B
Cluster A Maximal Linkage Cluster B Cluster A

18 Average Centroid Linkage
Cluster Cohesiveness Average Centroid Linkage Cluster B Cluster A 18

19 Generalization Coefficient
Search Tag = “Java” Generalization coefficient = 2 .25 .4 .55 .7 .85 1.0 Step = .15 Deals j2ee java Java Web Design Google Tools Search Shopping webDesign SearchEng Food Espresso Coffee Bargins Programming

20 Division Coefficient Division Coefficient=0.6 .25 .4 .55 .7 .85 1.0
Step = .15 Deals j2ee java Java Web Design Tools Google Search webDesign SearchEng Food Espresso Coffee Shopping Bargins Programming Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6

21 Personalization Java Coffee Programmimg Nestle StarBucks 1 URL
Morning_drink Nascafe Programmimg 2 URL Java Eclips C# Coffee 3 URL JSEE C++ Tourism 4 URL Java Rest_Bruney West_Malaizija 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
imp= 1 rp rb Coffee Drinker Java Coffee Programmimg URL Nestle StarBucks Morning_drink Nascafe Programmimg URL Programmer Java Eclips C# Coffee URL JSEE C++ Java Traveler Tourism URL Java Rest_Bruney West_Malaizija Coffee URL Indonesia_tours Australia

24 Delicious Dataset

25 Step Coefficient chart

26 Generalization Coefficient Chart

27 Division coefficient chart

28 Maximal Complete Link Tools Google Espresso Web Food Java Cluster1
SearchEng Google SearchEng Espresso Web Food Cluster2 Java Tools Web Java Cluster3 Espresso Food

29 Maximal Complete Link Clustering

30 K-Means Clustering K

31 Comparison of Clustering techniques

32 Conclusions & Future Work
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 Q/A?

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