SCIENCE CHINA Information Sciences, Volume 60, Issue 11: 110102(2017) https://doi.org/10.1007/s11432-016-9197-0

## Convolutional neural networks for expert recommendation in community question answering

• AcceptedJul 17, 2017
• PublishedOct 13, 2017
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### Abstract

Community Question Answering (CQA) is becoming an increasingly important web service for people to search for expertise and to share their own. With lots of questions being solved, CQA have built a massive, freely accessible knowledge repository, which can provide valuable information for the broader society rather than just satisfy the question askers. It is critically important for CQA services to get high quality answers in order to maximize the benefit of this process. However, people are considered as experts only in their own specialized areas. This paper is concerned with the problem of expert recommendation for a newly posed question, which will reduce the questioner's waiting time and improve the quality of the answer, so as to improve the satisfaction of the whole community. We proposean approach based on convolutional neural networks (CNN) to resolve this issue. Experimental analysis over a large real-world dataset from Stack Overflow demonstrates that our approach achieves asignificant improvement over several baseline methods.

### Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61572098, 61632011, 61562080), National Key Research Development Program of China (Grant No. 2016YFB1001103), and Major Projects of Science and Technology Innovation in Liaoning Province (Grant No. 20151060-21).

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• Figure 1

(Color online) The architecture of CNN used for expert recommendation.

• Figure 2

(Color online) Distribution of the most frequent tags in Stack Overflow.

• Figure 3

(Color online) Distribution of the most frequent co-occurringtags in Stack Overflow.

• Figure 4

(Color online) $S@1$ for prediction ofbest answerer using CNN model with different profile configurations.

• Figure 5

(Color online) $S@N$ for prediction ofbest answerer using CNN model based on titles and bodies.

• Figure 6

(Color online) Results of the prediction ofbest answerer based on D40: Y axis shows $S@1$ values and X axis showsthree models of different sentence length.

• Table 1   Tags selected for the training set
 Frequently co-occur Partially co-occur Rarely co-occur C# Python Django SQL SQL-Server CSS Linux Delphi Ruby Windows .NET Ruby-on-Rails Java JavaScript WPF C iPhone Android
• Table 2   Data statistics
 Data set ID Questions Best answerers All 479531 56055 D20 311857 4390 D40 248300 2064
• Table 3   Performance comparison of proposed model and traditional methods based on D40
 Method TF-IDF Language model LR LDA [5] SSRM [18] STM [5] CNN-non-static $S@1$ 0.0320 0.0310 0.0349 0.0578 0.0578 0.1034 0.2734 $S@2$ 0.0442 0.0372 0.0513 0.0765 0.0765 0.1051 0.2830 $S@3$ 0.0560 0.0442 0.0625 0.0810 0.0810 0.1192 0.2884 $S@4$ 0.0636 0.0478 0.0709 0.0836 0.0836 0.1200 0.2928 $S@5$ 0.0714 0.0524 0.0778 0.0856 0.0856 0.1267 0.2966

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