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数据分析与知识发现  2020, Vol. 4 Issue (11): 52-62     https://doi.org/10.11925/infotech.2096-3467.2020.0482
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
一种融合网络表示学习与XGBoost的评分预测模型*
丁勇1,2,陈夕1(),蒋翠清1,2,王钊1,2
1合肥工业大学管理学院 合肥 230009
2过程优化与智能决策教育部重点实验室 合肥 230009
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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摘要 

【目的】 基于丰富的元数据和评分数据,提出一种融合网络表示学习与XGBoost的评分预测模型——N2V_XGB。【方法】 提取并融合元数据和评分数据的相似性权重,构建同质关系网络;利用网络表示学习自动提取用户和项目特征,再将提取的特征作为XGBoost的输入,迭代训练获得最佳的评分预测模型。【结果】 实验表明,N2V_XGB模型的MAE和RMSE分别为0.686 7、0.873 7,低于4种主要的对比模型。【局限】 N2V_XGB模型未能很好地利用时间特征信息,评分结果没有反映时序变化。【结论】 N2V_XGB模型将网络表示学习与XGBoost算法进行有效融合,能够缓解数据稀疏,提高用户评分的预测精度。

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丁勇
陈夕
蒋翠清
王钊
关键词 网络表示学习XGBoost评分预测协同过滤Node2Vec    
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.

Key wordsNetwork Representation Learning    XGBoost    Rating Prediction    Collaborative Filtering    Node2Vec
收稿日期: 2020-05-28      出版日期: 2020-09-27
ZTFLH:  TP391  
基金资助:*本文系教育部人文社会科学规划基金项目“社会化媒体对企业绩效的影响机制研究”(15YJA630010);国家自然科学基金重点项目“大数据环境下的微观信用评价理论与方法研究”的研究成果之一(71731005)
通讯作者: 陈夕     E-mail: 1181738697@qq.com
引用本文:   
丁勇,陈夕,蒋翠清,王钊. 一种融合网络表示学习与XGBoost的评分预测模型*[J]. 数据分析与知识发现, 2020, 4(11): 52-62.
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.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0482      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I11/52
Fig.1  N2V_XGB模型框架
实体
(用户/项目)
属性特征
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
Table 1  元数据矩阵
Fig.2  用户同质网络图GU示例
Fig.3  Skip-gram的训练模式
Fig.4  不同的随机游走策略参数pq对结果的影响
Fig.5  不同特征向量维度d对结果的影响
模型参数
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
Table 2  XGBoost算法最优参数
指标

算法
Item-based_CF SVD_CF N2V_CF XGB_CF N2V_XGB
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
Table 3  N2V_XGB模型与对比模型的表现
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