Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (11): 52-62    DOI: 10.11925/infotech.2096-3467.2020.0482
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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.

Received: 28 May 2020      Published: 27 September 2020
 ZTFLH: TP391
Corresponding Authors: Chen Xi     E-mail: 1181738697@qq.com
 N2V_XGB Model Framework Metadata Matrix GU"> User Homogeneous Network Diagram $GU$ Training Mode of Skip-gram The Influence of Different p and q on the Result The Influence of Different Feature Vector Dimension d on the Result XGBoost Algorithm Optimal Parameters Performance of N2V_XGB Model and Comparison Model