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Learning to Recommend Questions Based on User Ratings

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).

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Learning to Recommend Questions Based on User Ratings

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  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 Paper Presentation 2/21

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

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

  5. Finding Questions Sort by popularity Paper Presentation 5/21

  6. PROBLEM STATEMENT Paper Presentation 6/21

  7. Recommendation Paper Presentation 7/21

  8. Preference Order Paper Presentation 8/21

  9. Ordered Pairs Paper Presentation 9/21

  10. Ranking Function Paper Presentation 10/21

  11. Principle Paper Presentation 11/21

  12. ALGORITHMS Paper Presentation 12/21

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

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

  15. EXPERIMENTS Paper Presentation 15/21

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

  17. Dataset (Cont.) Paper Presentation 17/21

  18. Dataset (Cont.) Paper Presentation 18/21

  19. Results • Evaluation Measure • Error rate of preference pairs • Result Paper Presentation 19/21

  20. Results (Cont.) 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 Paper Presentation 21/21

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