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Predicting Online Ratings with Network Representation Learning and XGBoost |
Ding Yong1,2,Chen Xi1(),Jiang Cuiqing1,2,Wang Zhao1,2 |
1School of Management, Hefei University of Technology, Hefei 230009, China 2Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei 230009, China |
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Abstract [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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Received: 28 May 2020
Published: 27 September 2020
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Corresponding Authors:
Chen Xi
E-mail: 1181738697@qq.com
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