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Predicting information seeker satisfaction in community question answering SIGIR 2008 dong Liu, Jiang Bian, Eugene Agichtein from Emory & Georgia Tech Uni

Predicting information seeker satisfaction in community question answering

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SIGIR 2008. Predicting information seeker satisfaction in community question answering. Yandong Liu, Jiang Bian , Eugene Agichtein from Emory & Georgia Tech University. CQA - Community Question Answering 問答 社 群 近來成為一個尋找資訊的有效方法. IS ASKER IN QA COMMUNITY SATISFIED?. - PowerPoint PPT Presentation

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Page 1: Predicting  information seeker satisfaction  in community question answering

Predicting information seeker satisfaction incommunity question answering

SIGIR 2008

Yandong Liu, Jiang Bian, Eugene Agichtein from Emory & Georgia Tech University

Page 2: Predicting  information seeker satisfaction  in community question answering

CQA -Community Question Answering

問答社群近來成為一個尋找資訊的有效方法

Page 3: Predicting  information seeker satisfaction  in community question answering

IS ASKER IN QA

COMMUNITY SATISFIED?

Page 4: Predicting  information seeker satisfaction  in community question answering

IS ASKER IN QA

COMMUNITY SATISFIED?發問者是否得到滿意的回答主宰著問答社群的發展或沒落

Page 5: Predicting  information seeker satisfaction  in community question answering

Asker use case

Lifecycle of a question in CQA

Page 6: Predicting  information seeker satisfaction  in community question answering

Asker use case

Lifecycle of a question in CQA

Page 7: Predicting  information seeker satisfaction  in community question answering

DefinitionAn asker in a CQAis considered satisfied iff:

The asker personally has closed the question, selected the best answer, and provided a rating of at least 3 stars for best answer quality.

Page 8: Predicting  information seeker satisfaction  in community question answering

DefinitionThe Asker Satisfaction Problem:

Given a question submitted by an asker is CQA, predict whether the user will be satisfied with the answers contributed by the community.

Page 9: Predicting  information seeker satisfaction  in community question answering

Features

Question Question-Answer Relationship Asker User History Answerer User History Category Features Textual Features

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Page 11: Predicting  information seeker satisfaction  in community question answering

Formally a two-class classification problembutprimarily focus on the satisfied class.

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Human judgment

Amazon’s paid rater service Mechanical Turk

Page 13: Predicting  information seeker satisfaction  in community question answering

Datasets

•Random 5000 from top 10000•Skewed distribution•Satisfaction varies by category

Page 14: Predicting  information seeker satisfaction  in community question answering

Setting

Methods Human Heuristic Baseline

Evaluation Metrics Precision Recall F1 Accuracy

ASP ASP_SVM ASP_RandomForest ASP_C4.5 ASP_Boosting ASP_NaiveBayes

Page 15: Predicting  information seeker satisfaction  in community question answering

Result

Page 16: Predicting  information seeker satisfaction  in community question answering

Selected features

Page 17: Predicting  information seeker satisfaction  in community question answering

Analysis

Online vs. Offline

Page 18: Predicting  information seeker satisfaction  in community question answering

Analysis

Feature Ablation

Page 19: Predicting  information seeker satisfaction  in community question answering

Analysis

Textual Features

Page 20: Predicting  information seeker satisfaction  in community question answering

Analysis

Past Experience

Page 21: Predicting  information seeker satisfaction  in community question answering

Conclusion

First to quantify and predict asker satisfaction

Shown importance of asker history is this

Our system outperform human assessors