ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (9): 2015-2026.doi: 10.7544/issn1000-1239.20210467

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Cross-Domain Trust Prediction Based on tri-training and Extreme Learning Machine

Wang Yan Tong Xiangrong   

  1. (School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005)
  • Online:2022-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62072392, 61972360) and the Major Innovation Project of Science and Technology of Shandong Province (2019522Y020131).

Abstract: Trust prediction is often used in recommendation systems and trading platforms. Most scholars study trust prediction based on one network. At present, most networks lack tags. Therefore, it is necessary to predict the social relationship of another network through one network. There are two problems in the method of using BP neural network combined with asymmetric tri-training to build a model. The first problem is that it takes a long time for BP neural network to backpropagate the adjustment error, and the second problem is that the model has only two classifiers to generate pseudo labels, which requires an expert threshold. To solve the structure and speed of the model, an improved cross-domain trust prediction model based on tri-training and extreme learning machine is proposed, which combines the tri-training model and asymmetric tri-training model to perform similar transfer learning methods to predict the network. The classifier of the model uses extreme learning machine with a faster speed, the tri-training model to generate pseudo-labels, and a “minority obeys majority” voting mechanism. Experiments test the effect of whether to add special features, and compare the algorithm with other existing algorithms on six data sets. Experiments show that the model is superior to other algorithms in terms of recall and stability.

Key words: tri-training, extreme learning machine, vote, transfer learning, pseudo label

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