Deep Learning Expert Ranking Method Based on Listwise
LI Xian-Hui, YU Zheng-Tao, WEI Si-Chao, GAO Sheng-Xiang, WANG Li-Ren
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504 Key Laboratory of Intelligent Information Processing, Kunming University of Science and Technology, Kunming 650504
Abstract The traditional expert list ranking method is easy to fall into local minimum, its training time is long, and the ranking function can not be approximated well. Combining listwise expert ranking with deep neural network, a deep learning expert ranking method based on listwise is proposed. Firstly, a deep learning expert ranking model is presented. Through unsupervised self-training, better parameters are obtained to initialize weights layer by layer. Then, the training instances formed by the expert documents corresponding to the queries are inputted into the restricted Boltzmann machines for the training. Finally, cosine value is used instead of matrix subtraction to compute weight. Thus, the whole replacement of weights is finished and the deep learning expert ranking model is constructed. The comparative experiments of expert ranking show that the proposed method is efficient and it improves the accuracy of ranking effectively.
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2015, Vol. 28 
