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A TopicRank Based Document Priors Model for Expert Finding

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Advanced Computational Methods in Life System Modeling and Simulation (ICSEE 2017, LSMS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 761))

Abstract

Document priors that encode our prior knowledge about the importance of different documents are essential to an expert finding system. This study proposed a TopicRank-based document priors model for expert finding. TopicRank algorithm is an extension of the DocRank algorithm. Latent dirichlet allocation was used to extract topics of the documents. We assumed there was a link between two documents that share common topics. Link analysis techniques were then used to obtain document priors. The proposed model was evaluated using the CSIRO Enterprise Research Collection and the results showed that the performance of the expert finding system was dramatically improved by introducing TopicRank-based document priors. In particular, Mean Average Precision increased 19.9% while Mean Reciprocal Rank rose as much as 23.4%.

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Acknowledgement

This work is supported by the Industrial Research Project of Science and Technology in Shaanxi Province (No. 2016GY-094), the Social Development Research Project of Science and Technology in Shaanxi Province (No. 2016SF-255), and the National Natural Science Foundation of China (Li Baojuan for No. 81301199, Liu Baohong for No. 61374185).

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Authors and Affiliations

  1. Network Center, Fourth Military Medical University, Xi’an, 710032, Shaanxi, China

    Jian Liu, Bei Jia, Hao Xu & Donghuai Gao

  2. College of Information System and Management, National University of Defense Technology, Changsha, 410073, Hunan, China

    Jian Liu & Baohong Liu

  3. Xi’an Communication Institute, Xi’an, 710106, Shaanxi, China

    Jian Liu, Bei Jia, Hao Xu, Baohong Liu, Donghuai Gao & Baojuan Li

  4. School of Biomedical Engineering, Fourth Military Medical University, Xi’an, 710032, Shaanxi, China

    Baojuan Li

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  1. Jian Liu
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  2. Bei Jia
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  3. Hao Xu
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  4. Baohong Liu
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  5. Donghuai Gao
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  6. Baojuan Li
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Corresponding author

Correspondence to Baojuan Li .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Minrui Fei

  2. Shanghai University, Shanghai, China

    Shiwei Ma

  3. Shanghai University, Shanghai, China

    Xin Li

  4. Shanghai University, Shanghai, China

    Xin Sun

  5. Shanghai University, Shanghai, China

    Li Jia

  6. Shanghai University, Shanghai, China

    Zhou Su

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Liu, J., Jia, B., Xu, H., Liu, B., Gao, D., Li, B. (2017). A TopicRank Based Document Priors Model for Expert Finding. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_33

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  • DOI: https://doi.org/10.1007/978-981-10-6370-1_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6369-5

  • Online ISBN: 978-981-10-6370-1

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