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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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[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.

Key wordsNetwork Representation Learning      XGBoost      Rating Prediction      Collaborative Filtering      Node2Vec     
Received: 28 May 2020      Published: 27 September 2020
ZTFLH:  TP391  
Corresponding Authors: Chen Xi     E-mail:

Cite this article:

Ding Yong,Chen Xi,Jiang Cuiqing,Wang Zhao. Predicting Online Ratings with Network Representation Learning and XGBoost. Data Analysis and Knowledge Discovery, 2020, 4(11): 52-62.

URL:     OR

N2V_XGB Model Framework
f1 f2 f3 fn
e1 f1,1 f1,2 f1,3 f1,n
e2 f2,1 f2,2 f2,3 f2,n
em fm,1 fm,2 fm,3 fm,n
Metadata Matrix
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
n_estimators 3 000
learning_rate 0.3
max_depth 4
min_child_weight 1
gamma 0.2
subsample 1
colsample_bytree 1
colsample_bylevel 1
reg_lambda 0.9
reg_alpha 0.1
seed 33
XGBoost Algorithm Optimal Parameters

MAE 1.128 3 1.072 8 0.804 3 0.706 5 0.686 7
RMSE 1.391 8 1.320 0 1.027 4 0.911 2 0.873 7
Performance of N2V_XGB Model and Comparison Model
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