Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (11): 52-62    DOI: 10.11925/infotech.2096-3467.2020.0482
Current Issue | Archive | Adv Search |
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
Download: PDF (978 KB)   HTML ( 17
Export: BibTeX | EndNote (RIS)      

[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
[1] 李晓菊. 协同过滤推荐系统中的数据稀疏性及冷启动问题研究[D]. 上海: 华东师范大学, 2018.
[1] ( Li Xiaoju. Research on Data Sparsity and Cold-Start Problem in Collaborative Filtering Recommender System[D]. Shanghai: East China Normal University, 2018.)
[2] 冷亚军, 陆青, 梁昌勇. 协同过滤推荐技术综述[J]. 模式识别与人工智能, 2014,27(8):720-734.
[2] ( Leng Yajun, Lu Qing, Liang Changyong. Survey of Recommendation Based on Collaborative Filtering[J]. Pattern Recognition and Artificial Intelligence, 2014,27(8):720-734.)
[3] 龚安, 高云, 高洪福. 一种基于项目属性评分的协同过滤推荐算法[J]. 计算机工程与科学, 2015,37(12):2366-2371.
[3] ( Gong An, Gao Yun, Gao Hongfu. A Collaborative Filtering Recommendation Algorithm Based on Ratings of Item Attributes[J]. Computer Engineering and Science, 2015,37(12):2366-2371.)
[4] 丁少衡, 姬东鸿, 王路路. 基于用户属性和评分的协同过滤推荐算法[J]. 计算机工程与设计, 2015,36(2):487-491, 497.
[4] ( Ding Shaoheng, Ji Donghong, Wang Lulu. Collaborative Filtering Recommendation Algorithm Based on User Attributes and Scores[J]. Computer Engineering and Design, 2015,36(2):487-491, 497.)
[5] Davoudi A, Chatterjee M. Product Rating Prediction Using Trust Relationships in Social Networks[C]// Proceedings of the 13th IEEE Annual Consumer Communications & Networking Conference, Las Vegas, NV, USA. IEEE, 2016.
[6] 肖志宇, 翟玉庆. 改进的基于信任网络和随机游走策略的评分预测模型[J]. 南京理工大学学报, 2015,39(5):602-608.
[6] ( Xiao Zhiyu, Zhai Yuqing. Improved Rating Prediction Model Basing on Trust Network and Random Walk Strategy[J]. Journal of Nanjing University of Science and Technology, 2015,39(5):602-608.)
[7] Davoudi A, Chatterjee M. Social Trust Model for Rating Prediction in Recommender Systems: Effects of Similarity, Centrality, and Social Ties[J]. Online Social Networks and Media, 2018,7:1-11.
doi: 10.1016/j.osnem.2018.05.001
[8] 薛福亮, 刘君玲. 基于用户间信任关系改进的协同过滤推荐方法[J]. 数据分析与知识发现, 2017,1(7):90-99.
[8] ( Xue Fuliang, Liu Junling. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. Data Analysis and Knowledge Discovery, 2017,1(7):90-99.)
[9] Ren Y, Li G, Zhang J, et al. Lazy Collaborative Filtering for Data Sets with Missing Values[J]. IEEE Transactions on Cybernetics, 2013,43(6):1822-1834.
pmid: 23757575
[10] 李征, 段垒. 基于用户兴趣评分填充的改进混合推荐方法[J]. 工程科学与技术, 2019,51(1):189-196.
[10] ( Li Zheng, Duan Lei. Improved Hybrid Recommendation Approach Based on User Interest Ratings Filling[J]. Advanced Engineering Sciences, 2019,51(1):189-196.)
[11] 彭石, 周志彬, 王国军. 基于评分矩阵预填充的协同过滤算法[J]. 计算机工程, 2013,39(1):175-178.
doi: 10.3969/j.issn.1000-3428.2013.01.037
[11] ( Peng Shi, Zhou Zhibin, Wang Guojun. Collaborative Filtering Algorithm Based on Rating Matrix Pre-filling[J]. Computer Engineering, 2013,39(1):175-178.)
doi: 10.3969/j.issn.1000-3428.2013.01.037
[12] Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems[J]. Computer, 2009,42(8):30-37.
[13] Li L, Zhang Y J. FastNMF: Highly Efficient Monotonic Fixed-Point Nonnegative Matrix Factorization Algorithm with Good Applicability[J]. Journal of Electronic Imaging, 2009,18(3):033004.
doi: 10.1117/1.3184771
[14] 毕华玲, 周微, 卢福强. 引入偏置的矩阵分解推荐算法研究[J]. 计算机应用研究, 2018,35(10):2928-2931, 2964.
[14] ( Bi Hualing, Zhou Wei, Lu Fuqiang. Bias Based Matrix Factorization Recommender Techniques[J]. Application Research of Computers, 2018,35(10):2928-2931, 2964.)
[15] 陈晔, 刘志强. 基于LFM矩阵分解的推荐算法优化研究[J]. 计算机工程与应用, 2019,55(2):116-120.
[15] ( Chen Ye, Liu Zhiqiang. Research on Improved Recommendation Algorithm Based on LFM Matrix Factorization[J]. Computer Engineering and Applications, 2019,55(2):116-120.)
[16] 何瑾琳, 刘学军, 徐新艳, 等. 融合Node2Vec和深度神经网络的隐式反馈推荐模型[J]. 计算机科学, 2019,46(6):41-48.
[16] ( He Jinlin, Liu Xuejun, Xu Xinyan, et al. Implicit Feedback Recommendation Model Combining Node2Vec and Deep Neural Networks[J]. Computer Science, 2019,46(6):41-48.)
[17] 杨贵军, 徐雪, 赵富强. 基于XGBoost算法的用户评分预测模型及应用[J]. 数据分析与知识发现, 2019,3(1):118-126.
[17] ( Yang Guijun, Xu Xue, Zhao Fuqiang. Predicting User Ratings with XGBoost Algorithm[J]. Data Analysis and Knowledge Discovery, 2019,3(1):118-126.)
[18] 马春平, 陈文亮. 基于评论主题的个性化评分预测模型[J]. 北京大学学报(自然科学版), 2016,52(1):165-170.
doi: 10.13209/j.0479-8023.2016.011
[18] ( Ma Chunping, Chen Wenliang. Personalized Model for Rating Prediction Based on Review Analysis[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2016,52(1):165-170.)
doi: 10.13209/j.0479-8023.2016.011
[19] 张红丽, 刘济郢, 杨斯楠, 等. 基于网络用户评论的评分预测模型研究[J]. 数据分析与知识发现, 2017,1(8):48-58.
[19] ( Zhang Hongli, Liu Jiying, Yang Sinan, et al. Predicting Online Users’ Ratings with Comments[J]. Data Analysis and Knowledge Discovery, 2017,1(8):48-58.)
[20] Grover A, Leskovec J. Node2Vec: Scalable Feature Learning for Networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 855-864.
[21] Chen T, Guesintr C. XGBoost: A Scalable Tree Boosting System[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 785-794.
[22] Deshpande M, Karypis G. Item-Based Top-N Recommendation Algorithms[J]. ACM Transactions on Information Systems, 2004,22(1):143-177.
doi: 10.1145/963770.963776
[23] Liang L, Tang R. An Improved Collaborative Filtering Algorithm Based on Node2Vec[C]// Proceedings of the 2nd International Conference on Computer Science and Artificial Intelligence. 2018: 218-222.
[24] 崔岩, 祁伟, 庞海龙, 等. 融合协同过滤和XGBoost的推荐算法[J]. 计算机应用研究, 2020,37(1):62-65.
[24] ( Cui Yan, Qi Wei, Pang Hailong, et al. Extreme Gradient Boosting Recommendation Algorithm with Collaborative Filtering[J]. Application Research of Computer, 2020,37(1):62-65.)
[1] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[2] Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[3] Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[4] Chuanming Yu,Haonan Li,Manyi Wang,Tingting Huang,Lu An. Knowledge Representation Based on Deep Learning:Network Perspective[J]. 数据分析与知识发现, 2020, 4(1): 63-75.
[5] Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[6] Shan Li,Yehui Yao,Hao Li,Jie Liu,Karmapemo. ISA Biclustering Algorithm for Group Recommendation[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
[7] Xiaofeng Li,Jing Ma,Chi Li,Hengmin Zhu. Identifying Commodity Names Based on XGBoost Model[J]. 数据分析与知识发现, 2019, 3(7): 34-41.
[8] Guijun Yang,Xue Xu,Fuqiang Zhao. Predicting User Ratings with XGBoost Algorithm[J]. 数据分析与知识发现, 2019, 3(1): 118-126.
[9] Li Jie,Yang Fang,Xu Chenxi. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[10] Wang Daoping,Jiang Zhongyang,Zhang Boqing. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[11] Wang Yong,Wang Yongdong,Guo Huifang,Zhou Yumin. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[12] Hua Lingfeng,Yang Gaoming,Wang Xiujun. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[13] Zhang Hongli,Liu Jiying,Yang Sinan,Xu Jian. Predicting Online Users’ Ratings with Comments[J]. 数据分析与知识发现, 2017, 1(8): 48-58.
[14] Xue Fuliang,Liu Junling. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[15] Qin Xingxin,Wang Rongbo,Huang Xiaoxi,Chen Zhiqun. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938