Learning to recommend questions based on user ratings
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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 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

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


Outline
Outline

  • Introduction

  • Problem Statement

  • Algorithms

  • Experiments

  • Conclusion

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Introduction
Introduction

  • Community-Based Question-Answering (CQA) Services

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Finding answers
Finding Answers

Query

Existed similar questions and their answers

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Finding questions
Finding Questions

Sort by popularity

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Problem statement
PROBLEM STATEMENT

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Recommendation
Recommendation

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Preference o rder
Preference Order

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Ordered pairs
Ordered Pairs

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Ranking function
Ranking Function

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Principle
Principle

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Algorithms
ALGORITHMS

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Experiments
EXPERIMENTS

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Dataset
Dataset

  • 297,919 questions under ‘travel’ category at Yahoo! Answers

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Dataset cont
Dataset (Cont.)

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Dataset cont1
Dataset (Cont.)

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Results
Results

  • Evaluation Measure

    • Error rate of preference pairs

  • Result

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Results cont
Results (Cont.)

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Conclusion
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

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