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Volume 2 Issue 4
Oct.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2015  >  2(4): 403-411
Shengxiang Gao, Zhengtao Yu, Linbin Shi, Xin Yan and Haixia Song, "Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 4, pp. 403-411, 2015.
Citation: Shengxiang Gao, Zhengtao Yu, Linbin Shi, Xin Yan and Haixia Song, "Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 4, pp. 403-411, 2015.
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Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship

  • Kunming University of Science and Technology, Kunming 650500, China

Funds:

This work was supported by National Natural Science Foundation of China (611750 68, 61472168, 61163004), Yunnan Provincial Natural Science Foundation (2013FA130), and Talent Promotion Project of Ministry of Science and Technology (2014HE001).

    Abstract
  • The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert's rating by using the historical rating records and the final decision results on the previous projects, and by means of some rules, we construct a rating matrix for projects and experts. For the data sparseness problem of the rating matrix and the “cold start” problem of new expert recommendation, we assume that those projects/experts with similar topics have similar feature vectors and propose a review expert collaborative recommendation algorithm based on topic relationship. Firstly, we obtain topics of projects/experts based on latent Dirichlet allocation (LDA) model, and build the topic relationship network of projects/experts. Then, through the topic relationship between projects/experts, we find a neighbor collection which shares the largest similarity with target project/expert, and integrate the collection into the collaborative filtering recommendation algorithm based on matrix factorization. Finally, by learning the rating matrix to get feature vectors of the projects and experts, we can predict the ratings that a target project will give candidate review experts, and thus achieve the review expert recommendation. Experiments on real data set show that the proposed method could predict the review expert rating more effectively, and improve the recommendation effect of review experts.

     

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