Skip to main content
SpringerLink
Log in

Mass Diffusion Recommendation Algorithm Based on Multi-subnet Composited Complex Network Model

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11633))

Included in the following conference series:

Abstract

Social recommendation algorithm that integrates social networks is widely used in big data information recommendation. However, there are many relationships among users of social networks, and the influence of each relationship on recommendation is different. Simply introducing a certain social relationship will inevitably affect the accuracy of recommendation algorithm. Based on multi-subnet composited complex network model, the multi-relationship composite network is constructed by loading multi-relationship social network on the user-commodity bipartite graph, and a mass diffusion recommendation algorithm based on multi-relationship composite network is proposed. The experimental results on real datasets Epioions and FilmTrust show that the proposed recommendation algorithm with two kinds of social relations has a significant improvement in recommendation accuracy and diversity compared with the recommendation algorithm with one kind of social relations and the traditional mass diffusion algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bawden, D., Holtham, C., Courtney, N.: Perspectives on information overload. Aslib Proc. 51(8), 249–255 (2013)

    Google Scholar 

  2. Liu, Y., Peng, H., Wang, J.: Verifiable diversity ranking search over encrypted outsourced data. Comput. Mater. Continua 55(1), 037 (2018)

    Google Scholar 

  3. Edmunds, A., Morris, A.: The problem of information overload in business organisations: a review of the literature. Int. J. Inf. Manage. 20(1), 17–28 (2000)

    Google Scholar 

  4. Meng, R., Rice, S.G., Wang, J., et al.: A fusion steganographic algorithm based on Faster R-CNN. Comput. Mater. Continua 55(1), 1 (2018)

    Google Scholar 

  5. Lee, B.K., Lee, W.N.: The effect of information overload on consumer choice quality in an on-line environment. Psychol. Mark. 21(3), 159–183 (2010)

    Google Scholar 

  6. Lü, L., Medo, M., Chi, H.Y., et al.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)

    Google Scholar 

  7. Guo, Q., Song, W., Hou, L., et al.: Effect of the time window on the heat-conduction information filtering medel. Phys. A 401(5), 15–21 (2014)

    Google Scholar 

  8. Chen, Z., Li, Z.: Collaborative filtering recommendation algorithm based on user characteristics and item attributes. J. Comput. Appl. 31(7), 1748–1750 (2011)

    Google Scholar 

  9. Zhang, Y.M., Wang, L., Cao, H.H., et al.: Recommendation algorithm based on user-interest-item tripartite graph. Pattern Recogn. Artif. Intell. 28(10), 913–921 (2015)

    Google Scholar 

  10. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)

    Google Scholar 

  11. Lu, Z., Dou, Z., Lian, J., et al.: Content-based collaborative filtering for news topic recommendation (2015)

    Google Scholar 

  12. Guo, N., Wang, B., et al.: Collaborative filtering recommendation algorithm based on characteristics of social network. J. Frontiers Comput. Sci. Technol. 12, 208–217 (2018)

    Google Scholar 

  13. Zhou, Z., Wang, Y.: Machine Learning and Application. Tsinghua University Press, Beijing (2007)

    Google Scholar 

  14. Wang, R., Ju, J., Li, S., et al.: Feature engineering for CRFs based opinion target extraction. J. Chin. Inf. Process. 26(2), 56–61 (2012)

    Google Scholar 

  15. Guo, Q., Song, W., Hu, Z., et al.: Non-equilibrium mass diffusion recommendation algorithm based on popularity. J. Comput. Appl. 35(12), 3502–3505 (2015)

    Google Scholar 

  16. Hu, J., Lin, X.: Design and implementation of recommendation algorithm based on user-socialized resource-vocabulary three-part graph. Inf. Stud. Theory Appl. 32(7), 130–134 (2016)

    Google Scholar 

  17. Zhou, T., Ren, J., Medo, M., et al.: Bipartite network projection and personal recommendation. Phys. Rev. E 76(4), 046115 (2007)

    Google Scholar 

  18. Watts, D., Strogatz, S.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)

    MATH  Google Scholar 

  19. Barabási, A.L., Bonabeau, E.: Scale-free networks. Sci. Am. 288(5), 60 (2003)

    Google Scholar 

  20. Sui, Y.: Research on multi-subnet composited complex network and its related properties. Qingdao University, Qingdao (2012)

    Google Scholar 

  21. Shao, F., Sun, R., Li, S.: Research of multi-subnet composited complex network and its operation. Complex Syst. Complex. Sci. 7(4), 20–25 (2012)

    Google Scholar 

  22. Sui, Y., Shao, F.J., Sun, R.C., et al.: Formalized descriptions of dynamic reorganizations of multi-subnet composited complex network based on vector space. J. Software 26(8), 2007–2019 (2015)

    MathSciNet  Google Scholar 

  23. Bin, S., Sun, G.: Important node detection algorithm for multiple relationships online social network based on multi-subnet composited complex network model. J. Nanjing Univ. (Nat. Sci.) 53(2), 378–385 (2017)

    MATH  Google Scholar 

  24. Zhu, Y., Lv, L.: Evaluation metrics for recommender systems. J. Univ. Electr. Sci. Technol. China 41(2), 163–175 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Qingdao University, Qingdao, 266071, China

    Zhou Shuang, Bin Sheng & Sun Gengxin

Authors
  1. Zhou Shuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sun Gengxin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sun Gengxin .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shuang, Z., Sheng, B., Gengxin, S. (2019). Mass Diffusion Recommendation Algorithm Based on Multi-subnet Composited Complex Network Model. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24265-7_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24264-0

  • Online ISBN: 978-3-030-24265-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation