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Jinwen Guo, Shengliang Xu, Shenghua Bao, and Yong Yu

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Presentation on theme: "Jinwen Guo, Shengliang Xu, Shenghua Bao, and Yong Yu"— Presentation transcript:

1 Jinwen Guo, Shengliang Xu, Shenghua Bao, and Yong Yu
Tapping on the Potential of Q&A Community by Recommending Answer Providers Jinwen Guo, Shengliang Xu, Shenghua Bao, and Yong Yu In Proc. of CIKM’08, 2008. Prepared and Presented by Baichuan Li

2 Outline Introduction & Motivation Generative Model for Q&A
User-Question-Answer Model Question Answerer Recommendation Experiments Conclusion 5/8/2019 Paper Presentation 2/21

3 Introduction Community Question-Answering (CQA) Services 3/21 5/8/2019
Paper Presentation 3/21

4 ? Motivation lag time in CQA low participation rate 4/21 5/8/2019
Paper Presentation 4/21

5 Question Answerer Recommendation
Question-based Recommendation Finding similar solved questions for new arrival questions first, and then recommending the answerers of these similar questions as the answerers for the new question. User-profile-based Recommendation Constructing user profiles first according to their history activities, and then suggest question answerers based on user-profiles. 5/8/2019 Paper Presentation 5/21

6 Similarity Term-level Topic-level Term-level + Topic-level
Text similarity Data sparseness Topic-level Latent topics Why not categories? Term-level + Topic-level 5/8/2019 Paper Presentation 6/21

7 Latent Dirichlet Allocation (LDA)
T: topic sets D: document sets Nd: number of distinct words in one document d z: topic θ: parameter of doc-topic distribution (multinomial) ф: parameter of topic-word distribution (multinomial) α: θ subject to Dirichlet (α) β: ф subject to Dirichlet (β) 5/8/2019 Paper Presentation 7/21

8 Notations 5/8/2019 Paper Presentation 8/21

9 Generative Model for Q&A
Topic Word Too many parameters to be estimated! Category 5/8/2019 Paper Presentation 9/21

10 Assumptions The topic space of question content is as same as that of answer content The users have the same prior distribution type over topics for asking and answering The parameters for topics prior distribution are identical in asking and answering for the same user 5/8/2019 Paper Presentation 10/21

11 User-Question-Answer Model
5/8/2019 Paper Presentation 11/21

12 Parameter Estimation 5/8/2019 Paper Presentation 12/21

13 Parameter Estimation (cont.)
5/8/2019 Paper Presentation 13/21

14 Parameter Estimation (cont.)
5/8/2019 Paper Presentation 14/21

15 Question Answerer Recommendation
Term-level (Content) Question-based (QST-BM25) User-profile-based (USER-BM25) Topic-level Question-based (QST-TOPIC) User-profile-based (USER-TOPIC) 5/8/2019 Paper Presentation 15/21

16 Question Answerer Recommendation (cont.)
Topic-level + Topic-level Term-level + Topic-level 5/8/2019 Paper Presentation 16/21

17 Dataset 5/8/2019 Paper Presentation 17/21

18 Topic Number Selection
5/8/2019 Paper Presentation 18/21

19 Discovered Topic Analysis
5/8/2019 Paper Presentation 19/21

20 Experimental Results 5/8/2019 Paper Presentation 20/21

21 Conclusion Question recommendation problem
Probabilistic generative model for user behavior The study of several methods’ performance Future work Term-level: other models (LM, TM, LTM, etc.) User availability Temporal dimension 5/8/2019 Paper Presentation 21/21

22 THANKS! 5/8/2019 Paper Presentation

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