1. Customized of Social Media Contents using Focused Topic Hierarchy
Speaker: Jim-An Tsai
Advisor: Jia-ling Koh
Author: Xingwei Zhu1, Zhao-Yan Ming2y, Yu Hao1, Xiaoyan Zhu1, Tat-Seng Chua
Date: 2015/1/8
Source: CIKM’14

2. Outline
Introduction
Problem Formulation
Method
Experiment
Conclusion

3. Introduction

4. General Taxonomy vs Focused Hierarchy
Challenges 1 2 3
General Taxonomy vs Focused Hierarchy
General Clustering vs Customized Organization

5. Outline
Introduction
Problem Formulation
Method
Experiment
Conclusion

6. User Information Needs
Problem Formulation
Social
Media
Corpus
User Information Needs

7. User Information Needs
Problem Formulation
Social Media Corpus
User Information Needs

8. Outline
Introduction
Problem Formulation
Method
Experiment
Conclusion

9. Method
Q1:How to discover the potentially useful topics for an information need?
Sol:

10. Focused Topic Discovering

11. Topic Hierarchy Generation

12. Topic Hierarchy Generation
If k = 5

13. Method
Q2:How to obtain the optimal topic structure to organize the users’ desired information?
Sol:

14. Topic Hierarchy Generation

16. Topic Hierarchy Construction Algorithm

17. Topic Hierarchy Construction Algorithm

18. Sol:
Problem1

19. Sol:
Problem2
Undirected Cycles:
Final Result:

20. Method
Q3:How to distinguish the representative contents for topics that directly meet the users; requirements?
Sol:

21. Customized Corpus Organization
Indicates the probability of the document’s content c given the topic v

22. Outline
Introduction
Problem Formulation
Method
Experiment
Conclusion

23. Experiment

24. Focused Topic Discovering

25. Focused Topic Hierarchy Construction

26. Focused Topic Hierarchy Construction

27. Customized Corpus Organization

28. Outline
Introduction
Problem Formulation
Method
Experiment
Conclusion

29. Conclusion
We proposed a novel method for customized social media organization using focused topic hierarchies, in which the social media contents can be organized into different structures to meet with different users’ personal information needs.
Future works: we will try to enhance the present framework with data from knowledge bases and social networks.

30. Thank for your listening