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Question microblog identification and answer recommendation

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Abstract

Consulting through message-updating in social network is regarded as a popular way of information seeking. However, most questions cannot receive answers or suggestions timely, and some questions even fail to get replies. Thus, identifying microblogs that contain questions (we call them “question microblog”) and recommending answers automatically are meaningful. We divide this problem into two submodules: question identification and answer recommendation. To the best of our knowledge, few attempts have been made to identify questions in microblogs due to standard features such as 5W1H (How, What, Where, When, Who, Why) are likely to be absent. The following challenging problem is how to provide users a relevant, credible, diversified and personalized answer after a microblog is recognized as a question. In this paper, we investigate the feasibility of integrating standard features and contextual features extracted from auxiliary resources and recommend a reasonable answer using collaborative filtering. Empirical results on Sina Microblog dataset demonstrate the efficacy and effectiveness of our method.

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Notes

  1. http://zhidao.baidu.com/.

  2. http://baike.baidu.com/.

  3. http://www-03.ibm.com/innovation/us/watson/.

  4. http://www.zhihu.com/.

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Acknowledgments

Xiangrong Liu is partially supported by National Natural Science Foundation of China (Grant Nos. 61373076, 60971085), the Fundamental Research Funds for the Central Universities (No. 2013121026), Natural Science Foundation of Fujian Province of China 2010J01350, Base Research Project of Shenzhen Bureau of Science, Technology, and Information (JC201006030858A). Chen Lin is partially supported by Shanghai Key Laboratory of Intelligent Information Processing under Grant No. IIPL-2011-004, China Natural Science Foundation under Grant Nos. NSFC61102136, NSFC61370010, NSFC81101115, the Natural Science Foundation of Fujian Province of China under Grant Nos. 2011J05158, 2011J01371, Fundamental Research Funds for Central Universities under Grant No. 2011121049, CCF-Tencent Open Research Fund under Grant No. CCF-Tencent20130101, Base Research Project of Shenzhen Bureau of Science,Technology, and Information under Grand No. JCYJ20120618155655087.

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Authors and Affiliations

  1. School of Information Science and Engineering, Xiamen University, Xiamen, China

    Xiangrong Liu, Runquan Xie, Chen Lin & Liujuan Cao

  2. Shenzhen Research Institute of Xiamen University, Shenzhen, China

    Xiangrong Liu & Chen Lin

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  1. Xiangrong Liu
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  2. Runquan Xie
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  3. Chen Lin
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Correspondence to Liujuan Cao.

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Liu, X., Xie, R., Lin, C. et al. Question microblog identification and answer recommendation. Multimedia Systems 22, 487–496 (2016). https://doi.org/10.1007/s00530-014-0411-z

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