S-DeepTrust: A deep trust prediction method based on sentiment polarity perception.

In online social networks, trust prediction is crucial for applications such as user decision-making and high-impact user recommendation. In recent years, various solutions to the trust prediction problem have been proposed in many academic literatures. However, the existing methods only focus on using more available information to achieve better prediction performance, while ignoring the influence of the authenticity of user information and the user subjectivity. In this paper, we apply the sentiment labels to the trust prediction process for the first time, and propose a deep trust prediction method based on sentiment polarity perception, S-DeepTrust. We first use transfer learning to generate sentiment labels for user review data and obtain a sentiment polarity matrix. Next, we design a rating matrix generation algorithm, which weights the sentiment polarity matrix and the original rating matrix to produce a new rating matrix. Then, the Siamese network architecture is used for the first time in the field of trust prediction to judge the trust relationships based on the extracted user preference vectors, which minimizes the representation of trust user pairs. Finally, the experiment results show that our method achieves higher prediction accuracy and is robust to trust relation sparsity compared with existing methods.