1. Predicting Long-Term Impact of CQA Posts: A Comprehensive Viewpoint
Yuan Yao
Joint work with
Hanghang Tong, Feng Xu, and Jian Lu
Aug 24-27, KDD 2014 1

2. Background and Motivations Modeling Multi-aspect Computation Speedup
Roadmap
Background and Motivations
Modeling Multi-aspect
Computation Speedup
Empirical Evaluations
Conclusions 2

3. Background and Motivations Modeling Multi-aspect Computation Speedup
Roadmap
Background and Motivations
Modeling Multi-aspect
Computation Speedup
Empirical Evaluations
Conclusions 3

4. CQA 4

5. What is the Long-Term Impact of a Q/A post?
Q: How many users will find it beneficial?
(Survey: we conduct a survey, asking different users about which is the best metric as the long-term impact; and 79.4% users choose the voting score. Analogy: to some extent, the voting score resembles the number of the citations that a research paper receives in the scientific publication domain. It reflects the net number of users who have a positive attitude toward it.)
What is the Long-Term Impact of a Q/A post?

6. Challenges Q: Why not off-the-shell data mining algorithms?
Challenge 1: Multi-aspect
C1.1. Coupling between questions and answers
C1.2. Feature non-linearity
C1.3. Posts dynamically arrive
Challenge 2: Efficiency 6

7. C1.1 Coupling Fq Fa Strong positive correlation! Predicted Q Impact Yq
Predicted Q Impact Yq
Predicted A Impact Ya Fq 1 Fa 2 3
Voting Consistency
Question Features
Answer Features
Y Yao, H Tong, T Xie, L Akoglu, F Xu, J Lu. Joint Voting Prediction for Questions and Answers in CQA. ASONAM 2014.

8. C1.1 Coupling LIP-M: [Yao+@ASONAM’14] Question prediction
Answer prediction 1 2 3
Voting Consistency
Regularization 8

9. C1.2 Non-linearity The kernel trick (e.g., SVM) Mercer kernel
Kernel matrix as new feature matrix 9

10. + C1.3 Dynamics Solution: recursive least squares regression (x1, y1)…
(xt, yt)
Current model
Existing examples
Modelt
New model
New examples +
Modelt+1
(xt+1, yt+1)
Solution: recursive least squares regression
[Haykin2005]
[Haykin2005] S Haykin. Adaptive filter theory

11. This Paper
Q1: how to comprehensively capture the multi-aspect in one algorithm?
Coupling, non-linearity, and dynamics
Q2: how to make the long-term impact prediction algorithm efficient? 11

12. Background and Motivations Modeling Multi-aspect Computation Speedup
Roadmap
Background and Motivations
Modeling Multi-aspect
Computation Speedup
Empirical Evaluations
Conclusions 12

13. Modeling Non-linearity
Basic Idea: kernelize LIP-M
Details - LIP-KM:
Closed-form solution:
Complexity: O(n3)
Question prediction
Answer prediction 1 2 3
Voting Consistency
Regularization 13

14. Modeling Dynamics Basic idea: recursively update LIP-KM
Details - LIP-KIM:
Complexity: O(n3) -> O(n2)
(matrix
inverse
lemma) 14

15. Background and Motivations Modeling Multi-aspect Computation Speedup
Roadmap
Background and Motivations
Modeling Multi-aspect
Computation Speedup
Empirical Evaluations
Conclusions 16

16. Approximation Method (1)
Basic idea: compress the kernel matrix
Details – LIP-KIMA:
1) Separate decomposition
2) Make decomposition reusable
3) Apply decomposition on LIP-KIM
Complexity: O(n2) -> O(n)
(Nyström approximation)
(SVD on X1)
n*n -> n*r
(Eigen-decomposition)
(Eigen-decomposition) 17

17. Approximation Method (2)
Basic idea: filter less informative examples
Details - LIP-KIMAA:
Complexity: O(n) -> <O(n)
Current model
New examples
(x1, y1)
(x2, y2) …
(xt, yt)
Existing examples
Modelt
(xt+1, yt+1)
Filtering
New model
LIP-KIMAA is built upon LIP-KIMA, +
Modelt+1 ? 18

18. Summary LIP-KIMAA <O(n) K: Non-linearity I: Dynamics M: Coupling
A: Approximation
LIP-KIMA O(n)
LIP-KIM O(n2)
LIP-KM O(n3)
LIP-KI (Recursive Kernel Ridge Regression)
LIP-IM
Each upward solid arrow makes the model more comprehensive
Each dash arrow makes the model more scalable
Shaded boxes: our proposed work in this paper
White boxes: existing work
LIP-K (Kernel Ridge Regression)
LIP-M (CoPs)
LIP-I (Recursive Ridge Regression)
Coupling
Non-linearity
Dynamics
Ridge Regression 19

19. Background and Motivations Modeling Multi-aspect Computation Speedup
Roadmap
Background and Motivations
Modeling Multi-aspect
Computation Speedup
Empirical Evaluations
Conclusions 20

20. Experiment Setup
Datasets (
Stack Overflow , Mathematics Stack Exchange
Features
Content (bag-of-words) & contextual features
Time
Initial set
Incremental set
Training set
Test set 21

21. Evaluation Objectives
O1: Effectiveness
How accurate are the proposed algorithms for long-term impact prediction?
O2: Efficiency
How scalable are the proposed algorithms? 22

22. Effectiveness Results
Our methods
(better)
root mean squared error
Comparisons with existing models. 23

23. Efficiency Results The speed comparisons. LIP-KIMAA (sub-linear) Ours
(better)
The speed comparisons. 25

24. Quality-Speed Balance-off
Our methods
(better) 26

25. Background and Motivations Modeling Multi-aspect Computation Speedup
Roadmap
Background and Motivations
Modeling Multi-aspect
Computation Speedup
Empirical Evaluations
Conclusions 27

26. Conclusions
A family of algorithms for long-term impact prediction of CQA posts
Q1: how to capture coupling, non-linearity, and dynamics?
A1: voting consistency + kernel trick + recursive updating
Q2: how to make the algorithms scalable?
A2: approximation methods
Empirical Evaluations
Effectiveness: up to 35.8% improvement
Efficiency: up to 390x speedup and sub-linear scalability 28

27. Authors: Yuan Yao, Hanghang Tong, Feng Xu, and Jian Lu
Thanks！
Q&A
Yuan Yao,
Authors: Yuan Yao, Hanghang Tong, Feng Xu, and Jian Lu