Skip to main content

Social Media Recommendation

  • Chapter
  • First Online:
Book cover Social Media Retrieval

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

Social media recommendation is foreseen to be one of the most important services to recommend personalized contents to users in online social network. It imposes great challenge due to the dynamical behavior of users and the large-scale volumes of contents generated by the users. In this chapter, we first present the principal concept of social media recommendation. Then we present the framework of social media recommendation, with a focus on two important types of recommendations: interest-oriented social media recommendation and influence-oriented social media recommendation. For each case, we present the design of the recommendation that takes both social property and content property into account, such as user relations, content similarities, and propagation patterns. Furthermore, we present theoretical results and observations on the social media recommendation approaches.

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 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
Hardcover Book
USD 169.99
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.

    2012:http://www.facebook.com

  2. 2.

    2012:http://www.twitter.com

  3. 3.

    2012:http://www.youtube.com

  4. 4.

    2012:http://www.flickr.com

  5. 5.

    http://www.telegraph.co.uk/technology/news/9033765/YouTube-uploads-hit-60-hours-per-minute.html

  6. 6.

    http://www.netflixprize.com

  7. 7.

    2012:http://www.linkedin.com/

  8. 8.

    2012:http://en.wikipedia.org/wiki/Social_influence

  9. 9.

    http://www.renren.com

References

  1. Agarwal, N., Liu, H., Tang, L., Yu, P.: Identifying the influential bloggers in a community. In: Proceedings of the ACM International Conference on Web Search and Web Data Mining, Palo Alto (2008)

    Google Scholar 

  2. Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas (2008)

    Google Scholar 

  3. Bakshy, E., Karrer, B., Adamic, L.: Social influence and the diffusion of user-created content. In: Proceedings of the ACM Conference on Electronic Commerce, Stanford (2009)

    Google Scholar 

  4. Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., Aly, M.: Video suggestion and discovery for Youtube: taking random walks through the view graph. In: Proceedings of the ACM WWW, Beijing (2008)

    Google Scholar 

  5. Bao, H., Chang, E.: Adheat: an influence-based diffusion model for propagating hints to match ads. In: Proceedings of the ACM WWW, Raleigh (2010)

    Google Scholar 

  6. Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  7. Cai, J., Candes, E., Shen, Z.: A singular value thresholding algorithm for matrix completion. Arxiv preprint Arxiv:0810.3286 (2008)

    Google Scholar 

  8. Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas (2008)

    Google Scholar 

  9. Cui, P., Wang, F., Liu, S., Ou, M., Yang, S., Sun, L.: Who should share what? Item-level social influence prediction for users and posts ranking. In: Proceedings of the ACM SIGIR International Conference on Research and Development in Information. ACM, New York (2011)

    Google Scholar 

  10. Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., et al.: The Youtube video recommendation system. In: Proceedings of the ACM Conference on Recommender Systems, Barcelona (2010)

    Google Scholar 

  11. Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis. In: Proceedings of the ACM WWW, Beijing (2008)

    Google Scholar 

  12. DuBois, T., Golbeck, J., Kleint, J., Srinivasan, A.: Improving recommendation accuracy by clustering social networks with trust. Recomm. Syst. Soc. Web 1–8 (2009)

    Google Scholar 

  13. Golbeck, J., Hendler, J.: FilmTrust: movie recommendations using trust in web-based social networks. In: Proceedings of the IEEE Consumer Communications and Networking Conference, Las Vegas (2006)

    Google Scholar 

  14. Goyal, A., Bonchi, F., Lakshmanan, L.: Learning influence probabilities in social networks. In: Proceedings of the ACM International Conference on Web Search and Data Mining, New York (2010)

    Google Scholar 

  15. Johnson, C.: Matrix completion problems: a survey. In: Matrix Theory and Applications, vol. 40, pp. 171–198. American Mathematical Society, Providence (1990)

    Google Scholar 

  16. Kalashnikov, D., Chen, Z., Mehrotra, S., Nuray-Turan, R.: Web people search via connection analysis. IEEE Trans. Knowl. Data Eng.20(11), 1550–1565 (2008)

    Article  Google Scholar 

  17. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington (2003)

    Google Scholar 

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

    Google Scholar 

  19. Krippendorff, K.: Content Analysis: An Introduction to Its Methodology. Sage, Thousand Oaks (2004)

    Google Scholar 

  20. Lin, C.: Projected gradient methods for nonnegative matrix factorization. Neural Comput.19(10), 2756–2779 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  21. Macdonald, C., Ounis, I.: Voting for candidates: adapting data fusion techniques for an expert search task. In: Proceedings of the ACM International Conference on Information and Knowledge Management. ACM, New York (2006)

    Google Scholar 

  22. McMillan, D., Chavis, D.: Sense of community: a definition and theory. J. Community Psychol.14(1), 6–23 (1986)

    Article  Google Scholar 

  23. Melville, P., Mooney, R., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the National Conference on Artificial Intelligence. AAAI, Menlo Park (2002)

    Google Scholar 

  24. Mooney, R., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the ACM Conference on Digital Libraries, San Antonio (2000)

    Google Scholar 

  25. Newman, M.: The structure and function of complex networks. SIAM Rev.45, 167–256 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  26. Pazzani, M., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 325–341. Springer, Berlin/New York (2007)

    Chapter  Google Scholar 

  27. Rennie, J., Srebro, N.: Fast maximum margin matrix factorization for collaborative prediction. In: Proceedings of the ACM International Conference on Machine Learning. ACM, New York (2005)

    Google Scholar 

  28. Schafer, J., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 291–324. Springer, Berlin/New York (2007)

    Chapter  Google Scholar 

  29. Strogatz, S.: Exploring complex networks.Nature 410(6825), 268–276 (2001)

    Google Scholar 

  30. Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris (2009)

    Google Scholar 

  31. Thelwall, M., Wilkinson, D., Uppal, S.: Data mining emotion in social network communication: gender differences in myspace. J. Am. Soc. Inf. Sci. Technol.61(1), 190–199 (2010)

    Article  Google Scholar 

  32. Walter, F., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Auton. Agents Multi-Agent Syst.16(1), 57–74 (2008)

    Article  Google Scholar 

  33. Wang, Z., Sun, L., Yang, S., Zhu, W.: Prefetching strategy in peer-assisted social video streaming. In: Proceedings of the ACM Multimedia, Scottsdale (2011)

    Google Scholar 

  34. Wang, Z., Sun, L., Zhu, W., Yang, S., Li, H., Wu, D.: Joint social and content recommendation for user generated videos in online social network. Technical report

    Google Scholar 

  35. Wasko, M., Faraj, S.: Why should i share? Examining social capital and knowledge contribution in electronic networks of practice. Mis Q.29, 35–57 (2005)

    Google Scholar 

  36. Weng, J., Lim, E., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the ACM International Conference on Web Search and Data Mining, New York (2010)

    Google Scholar 

  37. Wolfe, A.: Social network analysis: methods and applications. Am. Ethnol.24(1), 219–220 (1997)

    Article  Google Scholar 

  38. Xu, R., Wunsch, D., et al.: Survey of clustering algorithms. IEEE Trans. Neural Netw.16(3), 645–678 (2005)

    Article  Google Scholar 

  39. Yang, W., Dia, J., Cheng, H., Lin, H.: Mining social networks for targeted advertising. In: Proceedings of the IEEE Annual Hawaii International Conference on System Sciences. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  40. Zhou, R., Khemmarat, S., Gao, L.: The impact of YouTube recommendation system on video views. In: Proceedings of the ACM IMC, Melbourne (2010)

    Google Scholar 

  41. Ziegler, C., Lausen, G.: Propagation models for trust and distrust in social networks. Inf. Syst. Front.7(4), 337–358 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhi Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Wang, Z., Zhu, W., Cui, P., Sun, L., Yang, S. (2013). Social Media Recommendation. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4555-4_2

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4554-7

  • Online ISBN: 978-1-4471-4555-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics