Skip to main content
SpringerLink
Log in

Network-Based Models for Social Recommender Systems

  • Chapter
  • First Online:
Business and Consumer Analytics: New Ideas

Abstract

With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modelling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets.

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 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover 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.

    For the User-User model the reasoning is equivalent: each user is represented by a vector with all the items she has rated V u. The similarity between users would be computed as in Eq. (11.19) as sim(u, v).

References

  1. Castells M (2001) The Internet Galaxy: Reflections on the Internet, Business, and Society. (Oxford University Press, Inc., New York, NY, USA).

    Book  Google Scholar 

  2. Kalakota R, Robinson M (1999) e-Business. (Addison Wesley Roadmap for Success).

    Google Scholar 

  3. Brynjolfsson E, Hu Y, Smith MD (2003) Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Science 49(11):1580–1596.

    Article  Google Scholar 

  4. Netflix Prize Rankings. Hacking NetFlix. http://www.hackingnetflix.com/2006/10/. Accessed 09-March-2017

  5. The BellKor 2008 Solution to the Netflix Prize. http://www.research.att.com/people/Volinsky_Christopher_T/custom_index.html. Accessed 09-March-2017

  6. Netflix Prize Leaderboard 2009. http://www.netflixprize.com/leaderboard.html. Accessed 09-March-2017

  7. Lohr S (2009) A $1 million research bargain for Netflix, and maybe a model for others. The New York Times 22.

    Google Scholar 

  8. Bobadilla J, and Ortega F, and Hernando A and Gutiérrez A (2013) A. Recommender systems survey Knowledge-Based Systems 46:109–132.

    Article  Google Scholar 

  9. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. Acm Transactions on Information Systems 22:143–177.

    Article  Google Scholar 

  10. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms, WWW ‘01. (ACM, New York, NY, USA), pp. 285–295.

    Google Scholar 

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

    Article  Google Scholar 

  12. Mackey L, Weiss D, Jordan M I (2010) Mixed membership matrix factorization. Proceedings of the 27th International Conference on Machine Learning, pp 711–718.

    Google Scholar 

  13. Guimerà R, Sales-Pardo M (2009) Missing and spurious interactions and the reconstruction of complex networks. Proc. Natl. Acad. Sci. U. S. A. 106(52):22073–22078.

    Article  Google Scholar 

  14. Guimerà R, Llorente A, Moro E, Sales-Pardo M (2012) Predicting human preferences using the block structure of complex social networks. PLoS One 7(9):e44620.

    Article  Google Scholar 

  15. Godoy-Lorite A, Guimerà R, Moore C, Sales-Pardo M (2016) Accurate and scalable social recommendation using mixed-membership stochastic block models. Proc. Natl. Acad. Sci. U. S. A 113(50):14207–14212.

    Article  Google Scholar 

  16. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99(12):7821–7826.

    Article  MathSciNet  Google Scholar 

  17. Guimera R, Sales-Pardo M, Amaral LAN (2004) Modularity from fluctuations in random graphs and complex networks. Physical Review E 70(2):025101.

    Article  Google Scholar 

  18. Guimera R, Sales-Pardo M, Amaral LA (2007) Classes of complex networks defined by role-to-role connectivity profiles. Nature Physics 3(1):63–69.

    Article  Google Scholar 

  19. Fortunato S (2010) Community detection in graphs. Physics Reports 486(3):75–174.

    Article  MathSciNet  Google Scholar 

  20. Rombach MP, Porter MA, Fowler JH, Mucha PJ (2010) Core-Periphery Structure in Networks. Physics Reports 74(1):167–190.

    MathSciNet  MATH  Google Scholar 

  21. White HC, Boorman SA, Breiger RL (1976) Social structure from multiple networks. I. Blockmodels of roles and positions. American Journal of Sociology pp. 730–780.

    Article  Google Scholar 

  22. Rovira-Asenjo N, Gumí T, Sales-Pardo M, Guimerà R (2013) Predicting future conflict between team-members with parameter-free models of social networks. Scientific Reports 3:1999.

    Article  Google Scholar 

  23. Aicher C, Jacobs AZ, Clauset A (2014) Learning latent block structure in weighted networks. Journal of Complex Networks 3(2):221–248.

    Article  MathSciNet  Google Scholar 

  24. Peixoto TP (2017) Nonparametric weighted stochastic block models. arXiv preprint arXiv:1708.01432.

    Google Scholar 

  25. Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96(455):1077–1087.

    Article  MathSciNet  Google Scholar 

  26. Ekstrand MD, Ludwig M, Konstan JA, Riedl JT (2011) Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. Proceedings of the Fifth ACM Conference on Recommender Systems pp. 133–140.

    Google Scholar 

  27. Jaynes ET (2003) Probability Theory: The Logic of Science. (Cambridge University Press).

    Google Scholar 

  28. Airoldi EM, Blei DM, Fienberg SE, Xing EP (2008) Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9(2008):1981–2014.

    MATH  Google Scholar 

  29. Ball B, Karrer B, Newman ME (2011) Efficient and principled method for detecting communities in networks. Physical Review E 84(3):036103.

    Article  Google Scholar 

  30. Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. pp. 39–42.

    Google Scholar 

  31. Meeds E, Ghahramani Z, Neal RM, Roweis ST (2006) Modeling dyadic data with binary latent factors in Advances in Neural Information Processing Systems 19, eds. Schölkopf B, Platt J, Hoffman T. (MIT Press, Cambridge, MA), pp. 977–984.

    Google Scholar 

  32. Salakhutdinov R, Mnih A (2008) Probabilistic matrix factorization. Advances in Neural Information Processing Systems (NIPS ‘08) pp. 1257–1264.

    Google Scholar 

  33. Shan H, Banerjee A (2010) Generalized Probabilistic Matrix Community for Collaborative Filtering, Proceedings of the 2010 IEEE International Conference on Data Mining. (IEEE Computer Society, Washington, DC, USA), pp. 1025–1030.

    Google Scholar 

  34. Peixoto TP (2015) Model selection and hypothesis testing for large-scale network models with overlapping groups. Phys. Rev. X 5:011033.

    Google Scholar 

  35. Zhou T, Ren J, Medo M, Zhang Y-C (2007) Bipartite network projection and personal recommendation. Physical Review E 76(2007):046115

    Article  Google Scholar 

  36. Zhou T, Kuscsik Z, Liu J-G, Medo M, Wakeling JR, Zhang Y-C (2010) Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. U. S. A 107(2010):4511

    Article  Google Scholar 

  37. Yu F, Zeng A, Gillard S, Medo M (2016) Network-based recommendation algorithms: A review. Physica A: Statistical Mechanics and its Applications 452(2016):192–208.

    Article  Google Scholar 

  38. McAuley J, Targett C, Shi Q, van den Hengel A (2015) Image-Based Recommendations on Styles and Substitutes, SIGIR ‘15. (ACM, New York, NY, USA), pp. 43–52.

    Google Scholar 

  39. McAuley J, Pandey R, Leskovec J (2015) Inferring Networks of Substitutable and Complementary Products, KDD ‘15. (ACM, New York, NY, USA), pp. 785–794.

    Google Scholar 

  40. Brozovsky L, Petricek V (2007) Recommender System for Online Dating Service. (VSB, Ostrava).

    Google Scholar 

  41. Leo Y, Karsai M, Sarraute C, Fleury E (2016) Correlations of consumption patterns in social-economic networks. arXiv preprint arXiv:1609.03756.

    Google Scholar 

  42. Andreasen AR (2002) Marketing social marketing in the social change marketplace. Journal of Public Policy & Marketing 21(1):3–13.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, Imperial College London, London, UK

    Antonia Godoy-Lorite

  2. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain

    Roger Guimerà

  3. Departament d’Enginyeria, Química, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain

    Roger Guimerà

  4. Departament d’Enginyeria Química, Universitat Rovira i Virgili, Tarragona, Spain

    Marta Sales-Pardo

Authors
  1. Antonia Godoy-Lorite
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roger Guimerà
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marta Sales-Pardo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonia Godoy-Lorite .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Pablo Moscato

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Godoy-Lorite, A., Guimerà, R., Sales-Pardo, M. (2019). Network-Based Models for Social Recommender Systems. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-06222-4_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06221-7

  • Online ISBN: 978-3-030-06222-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation