research-article

Non-negative multiple matrix factorization with social similarity for recommender systems

Authors:

Jianhong GeAuthors Info & Claims

BDCAT '16: Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

Pages 280 - 286

https://doi.org/10.1145/3006299.3006323

Published: 06 December 2016 Publication History

Abstract

A key problem in online social networks is the identification of users' link information and the analysis of how these are reflected in the recommender systems. The basis to tackle this issue is user similarity measures. In this paper, we propose non-negative multiple matrix factorization with social similarity for recommender systems, considering the similarities between users, the relationships of users-resources and tags-resources. On this basis, we comparatively analyzed different performances of the recommendation with every similarity measure between users. In addition, our method can also recommend friends, resources, and tags to users. Experimental results on Lastfm and Delicious datasets show that the proposed method can significantly improve the recommendation accuracy compared with the art collaborative filtering methods.

References

[1]

B. Bringmann, M. Berlingerio, F. Bonchi, and A. Gionis. Learning and Predicting the Evolution of Social Networks. Intelligent Systems IEEE, 25(4):26--35, 2010.

[2]

P. Brodka, S. Saganowski, and P. Kazienko. GED: the method for group evolution discovery in social networks. Social Network Analysis & Mining, 3(1):1--14, 2013.

[3]

M. E. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E Statistical Nonlinear & Soft Matter Physics, 69(2 Pt 2):026113--026113, 2004.

[4]

H. Wu, Y. Hua, B. Li, and Y. Pei. Towards recommendation to trust-based user groups in social tagging systems. In: International Conference on Fuzzy Systems and Knowledge Discovery IEEE, pages 893--897. 2013.

[5]

K. Takeuchi, K. Ishiguro, A. Kimura, and H. Sawada. Non-negative multiple matrix factorization. In International Joint Conference on Artificial Intelligence, pages 1713--1720. ACM, 2013.

Digital Library

[6]

I. Guy, I. Ronen, and E. Wilcox. Do you know?: Recommending people to invite into your social network. In Proceedings of the 13th International Conference on Intelligent User Interfaces (IUI'09), pages 77--86. ACM, 2009.

Digital Library

[7]

D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In Proceedings of the 12th International Conference on Information and Knowledge Management (CIKM'03), pages 566--599. ACM, 2003.

Digital Library

[8]

J. Chen, W. Geyer, C. Dugan, M. Muller, and I. Guy. Make new friends, but keep the old: recommending people on social networking sites. In Proceedings of the 27th International Conference on Human Factors in Computing Systems, pages 201--210. ACM, 2009.

Digital Library

[9]

S. Lo and C. Lin. WMR-a graph-based algorithm for friend recommendation. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI'06), pages 121--128. ACM, 2006.

Digital Library

[10]

D. Ramage, S. Dumais, and D. Liebling. Characterizing microblogs with topic models. In Proceedings of International AAAI Conference on Weblogs and Social Media, pages 130--137. Citeseer, 2010.

[11]

V. Agarwal and K. K. Bharadwaj. A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Social Network Analysis & Mining, 3(3):359--379, 2013.

[12]

M. Chechev and P. Georgiev. A multi-view content-based user recommendation scheme for following users in twitter. In Social Informatics, pages 434--447. ACM, 2012.

Digital Library

[13]

J. Zhang, J. Tang, B. Liang, Z. Yang, S. Wang, J. Zuo, and J. Li. Recommendation over a Heterogeneous Social Network. In International Conference on Web-Age Information Management, pages 309--316. ACM, 2008.

Digital Library

[14]

T. Xu, Y. Chen, L. Jiao, B. Zhao, P. Hui, and X. Fu. Scaling Microblogging Services with Divergent Traffic Demands. In Proceedings of the 12th International Middleware Conference. pages 20--40. ACM, 2011.

Digital Library

[15]

G. Salton, and M. J. McGill. Introduction to Modern Information Retrieval. 1983.

Digital Library

[16]

P. Jaccard. Lois de distribution florale dans la zone alpine. Bulletin De La Societe Vaudoise Des Sciences Naturelles, 38(144):69--130, 1902.

[17]

T. Sørensen. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Dent Kong Dansk Vindensk, 5:1--34, 1948.

[18]

E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A. L. Barabasi. Hierarchical organization of modularity in metabolic networks. Science, 297(5586):1551--1555, 2002.

[19]

E. A. Leicht, P. Holme, and M. E. Newman. Vertex similarity in networks. Physical Review E Statistical Nonlinear & Soft Matter Physics, 73(2), 2006.

[20]

M. Molloy and B. Reed. The dominating number of a random cubic graph. Random Structures & Algorithms, 7(3):209--221, 1995.

[21]

A. Barabasi and R Albert. Emergence of scaling in random networks. Science, 286(5439): 509--512, 1999.

[22]

Y. Xie, T. Zhou, and B. Wang. Scale-free networks without growth. Physica A-statistical Mechanics and Its Applications, 387(7): 1683--1688, 2008.

[23]

Z. Huang, X. Li, and H. Chen. Link prediction approach to collaborative filtering. In Proceedings of the 5th ACM/IEEECS joint conference on Digital libraries, pages 141--142. ACM, 2005.

Digital Library

[24]

P. Holme, B. J. Kim, C. N. Yoon, and S. K. Han. Attack vulnerability of complex networks. Physical Review E, 65(5), 2002.

[25]

C. Yin, W. Wang, G. Chen, and B. Wang. Decoupling process for better synchronizability on scale-free networks. Physical Review E Statistical Nonlinear & Soft Matter Physics, 74(4 Pt 2):159--163, 2006.

[26]

G. Zhang, D. Wang, and G. Li. Enhancing the transmission efficiency by edge deletion in scale-free networks. Physical Review E Statistical Nonlinear & Soft Matter Physics, 76(1 Pt 2):55-86, 2007.

[27]

L. A. Adamic and E. Adar. How to search a social network. Social Networks, 27(3):187-203, 2005.

[28]

I. Cantador, P. Brusilovsky, and T. Kuflik. 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011). In Proceedings of the 5th ACM conference on Recommender systems. ACM, 2011.

Digital Library

[29]

J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1): 5-53, 2004.

Digital Library

[30]

T. Fawcett. Introduction to ROC analysis. Pattern Recognition Letters, 27(8):861-874, 2006.

Digital Library

[31]

K. Jarvelin and J. Kekalainen. IR evaluation methods for retrieving highly relevant documents. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 41-48. ACM, 2000.

Digital Library

Cited By

Ghalamkari KSugiyama M(2023)Non-negative low-rank approximations for multi-dimensional arrays on statistical manifoldInformation Geometry10.1007/s41884-023-00100-56:1(257-292)Online publication date: 24-Feb-2023
https://doi.org/10.1007/s41884-023-00100-5
Song WLi X(2019)A Non-Negative Matrix Factorization for Recommender Systems Based on Dynamic BiasModeling Decisions for Artificial Intelligence10.1007/978-3-030-26773-5_14(151-163)Online publication date: 24-Jul-2019
https://doi.org/10.1007/978-3-030-26773-5_14
Zhao JSun G(2018)Detect User’s Rating Characteristics by Separate Scores for Matrix Factorization TechniqueSymmetry10.3390/sym1011061610:11(616)Online publication date: 9-Nov-2018
https://doi.org/10.3390/sym10110616
Show More Cited By

Index Terms

Non-negative multiple matrix factorization with social similarity for recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

BDCAT '16: Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

December 2016

373 pages

ISBN:9781450346177

DOI:10.1145/3006299

Program Chairs:
Ashiq Anjum
University of Derby, UK
,
Xinghui Zhao
Washington State University, Vancouver, WA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2016

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Funding Sources

China Postdoctoral Science Foundation funded project
the National Natural Science Foundation of China

Conference

UCC '16

Sponsor:

SIGHPC

UCC '16: 9th International Conference on Utility and Cloud Computing

December 6 - 9, 2016

Shanghai, China

Acceptance Rates

Overall Acceptance Rate 27 of 93 submissions, 29%

Upcoming Conference

BDCAT '24

Sponsor:
sigarch

IEEE/ACM 11th International Conference on Big Data Computing, Applications and Technologies

December 16 - 19, 2024

Sharjah , United Arab Emirates

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Cited By

Ghalamkari KSugiyama M(2023)Non-negative low-rank approximations for multi-dimensional arrays on statistical manifoldInformation Geometry10.1007/s41884-023-00100-56:1(257-292)Online publication date: 24-Feb-2023
https://doi.org/10.1007/s41884-023-00100-5
Song WLi X(2019)A Non-Negative Matrix Factorization for Recommender Systems Based on Dynamic BiasModeling Decisions for Artificial Intelligence10.1007/978-3-030-26773-5_14(151-163)Online publication date: 24-Jul-2019
https://doi.org/10.1007/978-3-030-26773-5_14
Zhao JSun G(2018)Detect User’s Rating Characteristics by Separate Scores for Matrix Factorization TechniqueSymmetry10.3390/sym1011061610:11(616)Online publication date: 9-Nov-2018
https://doi.org/10.3390/sym10110616
Bobadilla JBojorque RHernando Esteban AHurtado R(2018)Recommender Systems Clustering Using Bayesian Non Negative Matrix FactorizationIEEE Access10.1109/ACCESS.2017.27881386(3549-3564)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2017.2788138

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