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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (1): 118-126    DOI: 10.11925/infotech.2096-3467.2018.0414
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Predicting User Ratings with XGBoost Algorithm
Guijun Yang1,Xue Xu1(),Fuqiang Zhao2
1China Center of Economics and Statistics Research, Tianjin University of Finance and Economics, Tianjin 300222, China
2Institute of Polytechnic, Tianjin University of Finance and Economics, Tianjin 300222, China
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[Objective] This study aims to build a model for effectively predicting ratings of user reviews and analysing consumer behaviours. [Methods] First, we applied the Latent Dirichlet Allocation model to set the topic features from user reviews as independent variable and user ratings as dependent variable. Then, we built a user rating prediction model based on the eXtreme Gradient Boosting algorithm. Finally, we added the disturbances of samples and attributes to the proposed model for rating prediction. [Results] We used the new model to predict user’s comments on a domestic automobile online portal, and identified their preferences of automobile. Compared with the Logical Regression and Random Forest algorithms, the proposed model has better precision and efficiency. [Limitations] We need to include data from other fields to more comprehensively describe user’s behaviours. [Conclusions] The proposed model could quantify user’s reviews and then predict their ratings effectively.

Key wordsRating Prediction      XGBoost Algorithm      LDA      Feature Extraction      User Reviews     
Received: 13 April 2018      Published: 04 March 2019

Cite this article:

Guijun Yang,Xue Xu,Fuqiang Zhao. Predicting User Ratings with XGBoost Algorithm. Data Analysis and Knowledge Discovery, 2019, 3(1): 118-126.

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