Recommendation algorithm based on modularity and label propagation

doi:10.11772/j.issn.1001-9081.2020010095

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (9): 2606-2612.DOI: 10.11772/j.issn.1001-9081.2020010095

• Data science and technology • Previous Articles Next Articles

Recommendation algorithm based on modularity and label propagation

SHENG Jun^1,2, LI Bin¹, CHEN Ling¹

1. School of Information Engineering, Yangzhou University, Yangzhou Jiangsu 225000, China;
2. School of Information Engineering, Yangzhou Polytechnic College, Yangzhou Jiangsu 225000, China

Received:2020-02-07 Revised:2020-04-20 Online:2020-09-10 Published:2020-04-28
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61379066, 61472344, 61402395), the Natural Science Foundation of Jiangsu Province(BK20140492), the Natural Science Fund of Jiangsu Provincial Department of Education (13KJB520026), the Six Talent Peaks Project in Jiangsu Province (2011-DZXX-032), the Jiangsu Higher Vocational College Professional Leader High-End Training Project (2019GRFX115), the Key Scientific Research Project of Yangzhou Polytechnic College (2018ZR04).

基于模块度和标签传递的推荐算法

盛俊^1,2, 李斌¹, 陈崚¹

1. 扬州大学信息工程学院, 江苏扬州 225000;
2. 扬州市职业大学信息工程学院, 江苏扬州 225000

通讯作者: 盛俊
作者简介:盛俊(1972-),男,江苏扬州人,副教授,硕士,CCF会员,主要研究方向:数据挖掘、复杂网络分析;李斌(1965-),男,江苏扬州人,教授,博士生导师,博士,CCF会员,主要研究方向:人工智能、知识工程;陈崚(1951-),男,江苏扬州人,教授,博士生导师,CCF会员,主要研究方向:数据挖掘、并行与分布式处理、复杂网络分析。
基金资助:
国家自然科学基金资助项目（61379066，61472344，61402395）；江苏省自然科学基金资助项目（BK20140492）；江苏省教育厅自然科学基金资助项目（13KJB520026）；江苏省六大人才高峰项目（2011-DZXX-032）；江苏省高等职业院校专业带头人高端研修项目（2019GRFX115）；扬州市职业大学校级重点科研项目（2018ZR04）。

Abstract

Abstract: To solve the problem of commodity recommendation based on network information, a recommendation algorithm based on community mining and label propagation on bipartite network was proposed. Firstly, a weighted bipartite graph was used to represent the user-item scoring matrix, and the label propagation technology was adopted to perform the community mining to the bipartite network. Then, the items which the users might be interested in were mined based on the community structure information of the bipartite network and by making full use of the similarity between the communities that the users in as well as the similarity between items and the similarity between the users. Finally, the item recommendation was performed to the users. The experimental results on real world networks show that, compared with the Collaborative Filtering recommendation algorithm based on item rating prediction using Bidirectional Association Rules (BAR-CF), the Collaborative Filtering recommendation algorithm based on Item Rating prediction (IR-CF), user Preferences prediction method based on network Link Prediction (PLP) and Modified User-based Collaborative Filtering (MU-CF), the proposed algorithm has the Mean Absolute Error (MAE) 0.1 to 0.3 lower, and the precision 0.2 higher. Therefore, the proposed algorithm can obtain recommendation results with higher quality compared to other similar methods.

Key words: social network, recommendation, bipartite graph, community mining, modularity label propagation

摘要： 针对基于网络信息的商品推荐的问题，提出了在二部网络上基于社区挖掘和标签传递的推荐算法。首先，用带权的二部图来表达用户-项目的评分矩阵，利用标签传递技术对二部网络进行社区挖掘；然后，基于二部网络中的社区结构信息，充分利用用户所在的社区之间的相似性以及项目之间、用户之间的相似性来挖掘用户可能感兴趣的项目；最后，向用户进行项目的推荐。在实际网络上的实验结果表明，与基于双向关联规则项目评分预测的推荐算法（BAR-CF）、基于项目评分预测的推荐算法（IR-CF）、基于网络链接预测的用户偏好预测方法（PLP）和改进的基于用户的协同过滤的方法（MU-CF）相比，该算法的平均绝对差（MAE）低0.1~0.3，准确率高0.2。因此，所提算法可以取得比其他类似方法更高质量的推荐结果。

关键词: 社交网络, 推荐, 二部图, 社区挖掘, 模块度标签传递

CLC Number:

TP393.02

SHENG Jun, LI Bin, CHEN Ling. Recommendation algorithm based on modularity and label propagation[J]. Journal of Computer Applications, 2020, 40(9): 2606-2612.

盛俊, 李斌, 陈崚. 基于模块度和标签传递的推荐算法[J]. 计算机应用, 2020, 40(9): 2606-2612.

References

[1] TIAN Y, ZHENG B, WANG Y, et al. College library personalized recommendation system based on hybrid recommendation algorithm[J]. Procedia CIRP, 2019, 83:490-494.
[2] BALAKRISHNAN J, CHENG C H, WONG K F, et al. Product recommendation algorithms in the age of omnichannel retailing-an intuitive clustering approach[J]. Computers and Industrial Engineering, 2018, 115:459-470.
[3] YANG F, XIE H, LI H. Video associated cross-modal recommendation algorithm based on deep learning[J]. Applied Soft Computing, 2019, 82:No. 105597.
[4] YIN C, WANG J, JONG J H. An improved recommendation algorithm for big data cloud service based on the trust in sociology[J]. Neurocomputing, 2017, 256:49-55.
[5] 许海玲,吴潇,李晓东,等. 互联网推荐系统比较研究[J]. 软件学报, 2009, 20(2):350-362. (XU H L, WU X, LI X D, et al. Comparative study on Internet recommendation system[J]. Journal of Software, 2009, 20(2):350-362.)
[6] LI W, CAO J, WU J, et al. A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks[J]. Future Generation Computer Systems, 2018, 88:262-270.
[7] LI J, XU Y, WANG Y, et al. Strongest association rules mining for efficient applications[C]//Proceedings of the 2007 IEEE Conference on Service Systems and Service Management. Piscataway:IEEE, 2007:1-6.
[8] 李杰,徐勇,王云峰,等. 面向个性化推荐的强关联规则挖掘[J]. 系统工程理论与实践, 2009, 29(8):144-152.(LI J, XU Y, WANG Y F, et al. Strongest association rules mining for personalized recommendation[J]. Systems Engineering-Theory and Practice, 2009, 29(8):144-152.)
[9] WANG F H, JIAN S Y. An effective content-based recommendation method for Web browsing based on keyword context matching[J]. Journal of Informatics and Electronics, 2006, 1(2):49-59.
[10] WARTENA C, SLAKHORST W, WIBBELS M, et al. Selecting keywords for content based recommendation[C]//Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York:ACM, 2010:1533-1536.
[11] 盛俊,陈崚. 基于隐空间映射的顶点带属性网络的链接预测[J]. 扬州大学学报(自然科学版), 2018, 21(4):57-60.(SHENG J, CHEN L. Link prediction based on latent space mapping in networks with nodes attributes[J]. Journal of Yangzhou University (Natural Science Edition), 2018, 21(4):57-60.)
[12] SHAHINPOUR S, BUTENKO S. Distance-based clique relaxations in networks:s-clique and s-club[C]//Proceedings of the 2nd International Conference on Network Analysis (Models, Algorithms, and Technologies for Network Analysis), PROMS 59. New York:Springer, 2013:149-174.
[13] VILLIERS C. The role of investor networks in transnational corporate governance networked governance[M]//FENWICK M, VAN UYTSEL S, WRBKA S. Networked Governance, Transnational Business and the Law. Berlin:Springer, 2014:285-313.
[14] KLIMEK P, JOVANOVIC A S, EGLOFF R, et al. Successful fish go with the flow:citation impact prediction based on centrality measures for term-document networks[J]. Scientometrics, 2016, 107(3):1265-1282.
[15] LI Y, WEN A, LIN Q, et al. Name disambiguation in scientific cooperation network by exploiting user feedback[J]. Artificial Intelligence Review, 2014, 41(4):563-578.
[16] GE M, LI A, WANG M. A bipartite network-based method for prediction of long non-coding RNA-protein interactions[J]. Genomics, Proteomics and Bioinformatics, 2016, 14(1):62-71.
[17] KAYA B, POYRAZ M. Unsupervised link prediction in evolving abnormal medical parameter networks[J]. International Journal of Machine Learning and Cybernetics, 2016, 7(1):145-155.
[18] ZHANG Y TAO C, JIANG G, et al. Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network[J]. Journal of Biomedical Semantics, 2014, 5:No. 33.
[19] KAYA B, POYRAZ M. Supervised link prediction in symptom networks with evolving case[J]. Measurement, 2014, 56:231-238.
[20] NIGAM A, CHAWLA N V. Link prediction in a semi-bipartite network for recommendation[C]//Proceedings of the 2016 Asian Conference on Intelligent Information and Database Systems, LNCS 9622. Berlin:Springer, 2016:127-135.
[21] WANG X, LIU Y, XIONG F. Improved personalized recommendation based on a similarity network[J]. Physica A:Statistical Mechanics and its Applications, 2016, 456:271-280.
[22] 万慧,周涛,鲍胜利. 基于社区划分的电影推荐模型[J]. 计算机应用, 2018, 38(S2):129-132.(WAN H, ZHOU T, BAO S L. Movie recommendation model based on community partition[J]. Journal of Computer Applications, 2018, 38(S2):129-132.)
[23] 黄泳航, 汤庸, 李春英,等. 基于社区划分的学术论文推荐模型[J]. 计算机应用, 2016, 36(5):1279-1283, 1289.(HUANG Y H, TANG Y, LI C Y, et al. Academic paper recommendation model based on community partition[J]. Journal of Computer Applications, 2016, 36(5):1279-1283, 1289.)
[24] 贺怀清, 范志亮, 刘浩翰. 基于网络社区划分的协同推荐算法[J]. 中国民航大学学报, 2016, 34(5):40-44.(HE H Q, FAN Z L, LIU H H. Collaborative filtering recommendation algorithm based on network community partition[J]. Journal of Civil Aviation University of China, 2016, 34(5):40-44.)
[25] NEWMAN M E J. Scientific collaboration networks. I. network construction and fundamental results[J]. Physical Review E:Statistical, Nonlinear, and Soft Matter Physics, 2001, 64(1 Pt 2):No. 016131.
[26] 徐晨. 基于二部网络分析的推荐算法研究及其应用[D]. 扬州:扬州大学, 2017:20-26.(XU C. Research and application of recommendation algorithm based on bipartite network analysis[D]. Yangzhou:Yangzhou University, 2017:20-26.)
[27] 刘枚莲,刘同存,张峰. 基于双向关联规则项目评分预测的推荐算法研究[J]. 武汉理工大学学报, 2011, 33(9):150-155.(LIU M L, LIU T C, ZHANG F. Recommendation algorithm based on item rating prediction using bidirectional association rules[J]. Journal of Wuhan University of Technology, 2011, 33(9):150-155.)
[28] 邓爱林,朱扬勇,施伯乐. 基于项目评分预测的协调过滤推荐算法[J]. 软件学报, 2003, 14(9):1621-1628.(DEN A L, ZHU Y Y, SHI B L. A collaborative filtering recommendation algorithm based on item rating prediction[J]. Journal of Software, 2003, 14(9):1621-1628.)
[29] 耿华. 基于链接预测模型的移动用户偏好预测方法的研究与实现[D]. 北京:北京邮电大学, 2013:8-20.(GENG H. Research and implementation of mobile user preference prediction method based on link prediction model[D]. Beijing:Beijing University of Posts and Telecommunications, 2013:8-20.)
[30] LUMAUAG R G, SISON A M, MEDINA R P. An enhanced recommendation algorithm based on modified user-based collaborative filtering[C]//Proceedings of the 4th International Conference on Computer and Communication Systems. Piscataway:IEEE, 2019:198-202.
[31] 盛俊,顾沈胜,陈崚. 基于隐空间映射的带符号网络上的顶点分类[J]. 计算机应用, 2019, 39(5):1411-1415.(SHENG J, GU S S, CHEN L. Node classification in signed networks based on latent space projection[J]. Journal of Computer Applications, 2019, 39(5):1411-1415.)

Recommendation algorithm based on modularity and label propagation

基于模块度和标签传递的推荐算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	WANG Zisen, LIANG Ying, LIU Zhengjun, XIE Xiaojie, ZHANG Wei, SHI Hongzhou. Matching method for academic expertise of research project peer review experts [J]. Journal of Computer Applications, 2021, 41(8): 2418-2426.
[2]	BAO Xuan, CHEN Hongmei, XIAO Qing. Time-incorporated point-of-interest collaborative recommendation algorithm [J]. Journal of Computer Applications, 2021, 41(8): 2406-2411.
[3]	LIU Huan, LI Xiaoge, HU Likun, HU Feixiong, WANG Penghua. Knowledge graph driven recommendation model of graph neural network [J]. Journal of Computer Applications, 2021, 41(7): 1865-1870.
[4]	ZHANG Meng, LI Weihua. Influence maximization algorithm based on user interactive representation [J]. Journal of Computer Applications, 2021, 41(7): 1964-1969.
[5]	YU Dunhui, ZHANG Luyi, ZHANG Xiaoxiao, MAO Liang. User recommendation method of cross-platform based on knowledge graph and restart random walk [J]. Journal of Computer Applications, 2021, 41(7): 1871-1877.
[6]	ZHANG Yang, DONG Shicheng. Intelligent recommendation method for lock mechanism in concurrent program [J]. Journal of Computer Applications, 2021, 41(6): 1597-1603.
[7]	SUN Heli, SUN Yuzhu, ZHANG Xiaoyun. Event description generation based on generative adversarial network [J]. Journal of Computer Applications, 2021, 41(5): 1256-1261.
[8]	LIN Yixing, TANG Hua. Hybrid recommendation model based on heterogeneous information network [J]. Journal of Computer Applications, 2021, 41(5): 1348-1355.
[9]	CHE Bingqian, ZHOU Dong. Tag recommendation method combining network structure information and text content [J]. Journal of Computer Applications, 2021, 41(4): 976-983.
[10]	YANG Li, WANG Shihui, ZHU Bo. Point-of-interest recommendation algorithm combing dynamic and static preferences [J]. Journal of Computer Applications, 2021, 41(2): 398-406.
[11]	SUN Heli, XU Tong, HE Liang, JIA Xiaolin. Personalized social event recommendation method integrating user historical behaviors and social relationships [J]. Journal of Computer Applications, 2021, 41(2): 324-329.
[12]	NAN Ning, YANG Chengyi, WU Zhihao. Multi-graph neural network-based session perception recommendation model [J]. Journal of Computer Applications, 2021, 41(2): 330-336.
[13]	ZHU Simiao, Wei Shiwei, WEI Siheng, YU Dunhui. Video recommendation algorithm based on danmaku sentiment analysis and topic model [J]. Journal of Computer Applications, 2021, 41(10): 2813-2819.
[14]	REN Kezhou, PENG Furong, GUO Xin, WANG Zhe, ZHANG Xiaojing. Social recommendation based on dynamic integration of social information [J]. Journal of Computer Applications, 2021, 41(10): 2806-2812.
[15]	WEN Wen, LIU Fang, CAI Ruichu, HAO Zhifeng. Dynamic recommendation algorithm for group-users' temporal behaviors [J]. Journal of Computer Applications, 2021, 41(1): 60-66.