1. Learning to Recommend Questions Based on User Ratings Ke Sun, Yunbo Cao, Xinying Song, Young-In Song, Xiaolong Wang and Chin-Yew Lin. In Proceeding of the 18th ACM Conference on Information and Knowledge Management (Hong Kong, China, November 02 - 06, 2009). Prepared and Presented by Baichuan Li

2. Outline Introduction Problem Statement Algorithms Experiments Conclusion 9/4/2015 Paper Presentation 2/21

3. Introduction Community-Based Question-Answering (CQA) Services 9/4/2015 Paper Presentation 3/21

4. Finding Answers 9/4/2015 Paper Presentation Query Existed similar questions and their answers 4/21

5. Finding Questions 9/4/2015 Paper Presentation Sort by popularity 5/21

6. PROBLEM STATEMENT 9/4/2015 Paper Presentation 6/21

7. Recommendation 9/4/2015Paper Presentation 7/21

8. Preference Order 9/4/2015 Paper Presentation 8/21

9. Ordered Pairs 9/4/2015 Paper Presentation 9/21

10. Ranking Function 9/4/2015 Paper Presentation 10/21

11. Principle 9/4/2015 Paper Presentation 11/21

12. ALGORITHMS 9/4/2015Paper Presentation 12/21

13. The Perceptron Algorithm for Preference Learning (PAPL) 9/4/2015 Paper Presentation 13/21

14. The Majority-Based Perceptron Algorithm (MBPA) 9/4/2015 Paper Presentation 14/21

15. EXPERIMENTS 9/4/2015Paper Presentation 15/21

16. Dataset 297,919 questions under ‘travel’ category at Yahoo! Answers 9/4/2015 Paper Presentation 16/21

17. Dataset (Cont.) 9/4/2015 Paper Presentation 17/21

18. Dataset (Cont.) 9/4/2015 Paper Presentation 18/21

19. Results Evaluation Measure ◦ Error rate of preference pairs Result 9/4/2015 Paper Presentation 19/21

20. Results (Cont.) 9/4/2015 Paper Presentation 20/21

21. Conclusion Investigated the problem of learning to recommend questions based on user ratings ◦ Enlarged the size of available training data through adding questions without user rating ◦ Demonstrated the approach’s effectiveness through intensive experiments Q&A 9/4/2015 Paper Presentation 21/21