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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.

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Presentation on theme: "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."— Presentation transcript:

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 , 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


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