Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2013

Abstract

Community Question Answering (CQA) sites are becoming increasingly important source of information where users can share knowledge on various topics. Although these platforms bring new opportunities for users to seek help or provide solutions, they also pose many challenges with the ever growing size of the community. The sheer number of questions posted everyday motivates the problem of routing questions to the appropriate users who can answer them. In this paper, we propose an approach to predict the best answerer for a new question on CQA site. Our approach considers both user interest and user expertise relevant to the topics of the given question. A user’s interests on various topics are learned by applying topic modeling to previous questions answered by the user, while the user’s expertise is learned by leveraging collaborative voting mechanism of CQA sites. We have applied our model on a dataset extracted from StackOverflow, one of the biggest CQA sites. The results show that our approach outperforms the TF-IDF based approach.

Discipline

Communication Technology and New Media | Databases and Information Systems

Publication

Social Informatics: SocInfo 2013 International Workshops, QMC and HISTOINFORMATICS

Volume

8359

First Page

55

Last Page

68

ISBN

9783642552854

Identifier

10.1007/978-3-642-55285-4_5

Publisher

Springer Verlag

City or Country

Kyoto, Japan

Comments

Workshop of Quality, Motivation and Coordination of Open Collaboration

Additional URL

http://doi.org/10.1007/978-3-642-55285-4_5

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