[Objective] This paper proposes a model to predict online ratings with the help of network representation learning and XGBoost—N2V_XGB. [Methods] First, we retrieved metadata and existing online rating data. Then, we extracted and merged the similarity weights of collected data to construct a homogenous relationship network. Third, we used network representation learning to automatically extract user and item features. Finally, we input these data to XGBoost, and obtained the best model with iteratively training. [Results] The MAE and RMSE of the proposed N2V_XGB model were 0.686 7 and 0.873 7, which were lower than the four classic models. [Limitations] We did not make good use of time features and the prediction results did not reflect time-series changes. [Conclusions] The proposed N2V_XGB model effectively address the data sparseness issues and improve the prediction accuracy of user ratings.
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