Skip to main content

Toward a Social Graph Recommendation Algorithm: Do We Trust Our Friends in Movie Recommendations?

  • Conference paper
On the Move to Meaningful Internet Systems: OTM 2012 Workshops (OTM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7567))

Abstract

Social networks provide users with information about their friends, their activities, and their preferences. In this paper we study the effectiveness of movie recommendations computed from such communicated preferences. We present a set of social movie recommendation algorithms, which we implemented on top of the Facebook social network, and we compare their effectiveness in influencing user decisions. We also study the effect of showing users a justification for the recommendations, in the form of the profile pictures of the friends that caused the recommendation.

We show that social movie recommendations are generally accurate. Furthermore, 80% of the users that are undecided on whether to accept a recommendation are able to reach a decision upon learning of the identities of the users behind the recommendation. However, in 27% of the cases, they decide against watching the recommended movies, showing that revealing identities can have a negative effect on recommendation acceptance.

This Work was supported in Part by a Gift from Google, Inc.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdul-Rahman, A., Hailes, S.: Supporting trust in virtual communities. In: Hawaii International Conference on System Sciences. Maui (2000)

    Google Scholar 

  2. Adomavicius, G., Tuzhili, A.: Towards the Next Gen. of Recommender Systems. IEEE Transactions on Knowledge and Data Engineering 17, 634–749 (2005)

    Article  Google Scholar 

  3. Asch, S.E.: Opinions and social pressure. Scientific American 193, 31–35 (1955)

    Article  Google Scholar 

  4. Burke, R.: Hybrid Recommender Systems. User Mod. and User-Adap 12, 331–370 (2002)

    Article  MATH  Google Scholar 

  5. Centola, D.: The Spread of Behavior in an Online Social Network Experiment. Science 329, 1194–1197 (2010)

    Article  Google Scholar 

  6. Durao, F., Dolog, P.: A Personalized Tag-Based Recommendation in Social Web Systems. In: Intelligent Web and Information Systems.CoRR (2012)

    Google Scholar 

  7. Key Facts Statistics, http://newsroom.fb.com/content/default.aspx?NewsAreaId=22

  8. Golbeck, J.:Generating Predictive Movie Recommendations from Trust in Social Networks, iTrust (2006)

    Google Scholar 

  9. Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: ACM Conference on Computer Supported Cooperative Work, pp. 241–250. ACM, New York (2000)

    Chapter  Google Scholar 

  10. Huang, Z., Zeng, D., Chen, H.: A Link Analysis Approach to Recommendation under Sparse Data. In: Americas Conference on Information Systems (2004)

    Google Scholar 

  11. Jøsang, A., Quattrociocchi, W., Karabeg, D.: Taste and Trust. In: Wakeman, I., Gudes, E., Jensen, C.D., Crampton, J. (eds.) Trust Management V. IFIP AICT, vol. 358, pp. 312–322. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Resnick, P., Lacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering. In: ACM Conference on Computer Supported Cooperative Work, New York (1994)

    Google Scholar 

  13. Ricci, F., Rokach L.: Recommender Systems Handbook. Springer, New York (2011)

    Google Scholar 

  14. Sarwar, B.M., Karypis, G., Onstan, J.A., Riedl, J.: Recommender Systems for Large-scale E-Commerce. In: ICCIT (2002)

    Google Scholar 

  15. Social Recommender Systems Methods and User Issues, http://hci.epfl.ch/teaching/advanced-hci/slides/2011.5.23_Yu.pdf

  16. Smith, B., Briggs, P., Coyle, M., O’Mahony, M.: Google Shared.A Case-Study in Social Search. In: International Conference, UMAP, 17 (2009)

    Google Scholar 

  17. Suri, A., Watts, D.J.: Cooperation and Contagion in Web-Based, Networked Public Goods Experiments. PLoS ONE, 6 e16836 (2011).

    Google Scholar 

  18. Su, X., Khoshgoftaar, T.: A Survey of Collaborative Filtering Techniques. In: Advances in Artificial Intelligence, 17 (2009)

    Google Scholar 

  19. Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 393–408 (1999)

    Google Scholar 

  20. Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences. iTrust, Heraklion (2005)

    Google Scholar 

  21. Melville, P., Sindhwani, V.: Recommender Systems. Encyclopedia of Machine Learning, New York (2010)

    Google Scholar 

  22. Vatturi, P.K., Geyer, W., Dugan, C., Muller, B.B.: Tag-based ltering for personalized bookmark recommendations. In: 17th ACM Conference on Information and Knowledge Mining, pp. 1395–1396. ACM, New York (2008)

    Google Scholar 

  23. Ziegler, C., Lausen, G.: Analyzing Correlation Between Trust and User Similarity in Online Communities. In: International Conference on Trust Management, vol.(2) (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Adabi, A., de Alfaro, L. (2012). Toward a Social Graph Recommendation Algorithm: Do We Trust Our Friends in Movie Recommendations?. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2012 Workshops. OTM 2012. Lecture Notes in Computer Science, vol 7567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33618-8_83

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33618-8_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33617-1

  • Online ISBN: 978-3-642-33618-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics