Skip to main content
SpringerLink
Log in

A Fuzzy Information Propagation Algorithm for Social Network Based Recommender Systems

  • Conference paper
  • First Online:
Information Technology and Computational Physics (CITCEP 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 462))

Abstract

Web-based services that have become prevalent in people’s everyday life generate huge amounts of data, which makes it hard for the users to search and discover interesting information. Therefore, tools for selecting and delivering personalized contents for users are crucial components of modern web applications. Social recommender systems suggest items to users assuming the knowledge of the users’ social network. This new approach can alleviate the common weaknesses of traditional recommender systems, which completely ignore the users’ personal relationships in the recommendation process. In this paper, a social network based fuzzy recommendation technique is presented, which propagates information through the users’ social network and predicts how users would probably like a certain product in the future. Experimental results on a public dataset show that the proposed method can significantly outperform popular and widely used recommendation system methods in terms of recommendation coverage while maintaining prediction accuracy and performs especially well for cold start users, that have only rated a few items or no item at all previously.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Notes

  1. 1.

    http://www.flixster.com.

References

  1. Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer (2011)

    Google Scholar 

  2. Jafarkarimi, H., Sim, A.T.H., Saadatdoost, R.: A naive recommendation model for large databases. Int. J. Inf. Educ. Technol. 2(3), 216–219 (2012)

    Google Scholar 

  3. Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Recommender Systems Handbook, pp. 73–105. Springer (2011)

    Google Scholar 

  4. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference On Computer Supported Cooperative Work, pp. 175–186. ACM (1994)

    Google Scholar 

  5. Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  6. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  7. Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: On the Move to Meaningful Internet Systems, : CoopIS, DOA, and ODBASE pp. 492–508. Springer (2004)

    Google Scholar 

  8. Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM (2009)

    Google Scholar 

  9. A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135–142. ACM (2010)

    Google Scholar 

  10. Avesani, P., Massa, P., Tiella, R.: A trust-enhanced recommender system application: Moleskiing. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 1589–1593. ACM (2005)

    Google Scholar 

  11. O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 167–174. ACM (2005)

    Google Scholar 

  12. Massa, P., Avesani, P.: Trust-aware recommender systems,. In: Proceedings of the 2007 Acm Conference on Recommender Systems, pp. 17–24. ACM (2007)

    Google Scholar 

  13. Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference On Information And Knowledge Management, pp. 931–940. ACM (2008)

    Google Scholar 

  14. Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference On Research And Development in Information Retrieval, pp. 203–210. ACM (2009)

    Google Scholar 

  15. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I., Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2011)

    Google Scholar 

  16. Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy computational models for trust and reputation systems. Electron. Commer. Res. Appl. 8(1), 37–47 (2009)

    Article  Google Scholar 

  17. Andersen, R., Borgs, C., Chayes, J., Feige, U., Flaxman, A., Kalai, A., Mirrokni, V., Tennenholtz, M.: Trust-based recommendation systems: an axiomatic approach. In: Proceedings of the 17th International Conference on World Wide Web, pp. 199–208. ACM (2008)

    Google Scholar 

  18. Jsang, A., Ismail, R.: The beta reputation system. In: Proceedings of the 15th Bled Electronic Commerce Conference, vol. 5, pp. 2502–2511 (2002)

    Google Scholar 

  19. Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  20. Pósfai, M., Kóczy.: Idf-social: an information diffusion-based fuzzy model for social recommender systems. In: The Congress on Information Technology, Computational and Experimental Physics (CITCEP 2015), pp. 106–112. (2015)

    Google Scholar 

  21. Guha, R., Kumar, R., Raghavan, P., and Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th international conference on World Wide Web, pp.403–412. ACM (2004)

    Google Scholar 

  22. Kóczy, L.T., Tikk, D.: Fuzzy rendszerek. Typotex, Budapest (2000)

    MATH  Google Scholar 

  23. Yager, R.R.: On the measure of fuzziness and negation II. Lattices. Inf. Control 44(3), 236–260 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  24. Dombi, J.: A general class of fuzzy operators, the demorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets Syst. 8(2), 149–163 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  25. Zadeh, L.A.: Fuzzy sets. Inf. control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  26. Dubois, D.J.: Fuzzy Sets And Systems: theory And Applications. Academic pres. vol. 144 (1980)

    Google Scholar 

  27. Hamacher, H.: Über logische Verknüpfungen unscharfer Aussagen und deren zugehörige Bewertungsfunktionen. (1975)

    Google Scholar 

  28. Schweizer, B., Sklar, A.: Associative functions and abstract semigroups. Publ. Math. Debrecen 10, 69–81 (1963)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The research was supported by National Research, Development and Innovation Office (NKFIH) K105529, K108405.

Author information

Authors and Affiliations

  1. Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary

    Gergely Posfai, Gabor Magyar & Laszlo T. Koczy

  2. Szechenyi Istvan University, Gyor, Hungary

    Laszlo T. Koczy

Authors
  1. Gergely Posfai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabor Magyar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laszlo T. Koczy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gergely Posfai .

Editor information

Editors and Affiliations

  1. Faculty of Physics & Applied Comp. Sci, AGH University of Science and Technology Faculty of Physics & Applied Comp. Sci, Kraków, Poland

    Piotr Kulczycki

  2. Faculty of Engineering Sciences, Széchenyi István University Faculty of Engineering Sciences, Győr, Hungary

    László T. Kóczy

  3. Faculty of Civil Engineering, Slovak University of Technology Faculty of Civil Engineering, Bratislava, Slovakia

    Radko Mesiar

  4. Systems Research Institute, Polish Academy of Sciences Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Posfai, G., Magyar, G., Koczy, L.T. (2017). A Fuzzy Information Propagation Algorithm for Social Network Based Recommender Systems. In: Kulczycki, P., Kóczy, L., Mesiar, R., Kacprzyk, J. (eds) Information Technology and Computational Physics. CITCEP 2016. Advances in Intelligent Systems and Computing, vol 462. Springer, Cham. https://doi.org/10.1007/978-3-319-44260-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44260-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44259-4

  • Online ISBN: 978-3-319-44260-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation