Using Behavioral Data Mining to Produce Friend Recommendations in a Social Bookmarking System

  • Matteo Manca
  • Ludovico Boratto
  • Salvatore Carta
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 178)


Social recommender systems have been developed to filter the large amounts of data generated by social media systems. A type of social media, known as social bookmarking system, allows the users to tag bookmarks of interest and to share them. Although the popularity of these systems is increasing and even if users are allowed to connect both by following other users or by adding them as friends, no friend recommender system has been proposed in the literature. Behavioral data mining is a useful tool to extract information by analyzing the behavior of the users in a system. In this paper we first perform a preliminary analysis that shows that behavioral data mining is effective to discover how similar the preferences of two users are. Then, we exploit the analysis of the user behavior to produce friend recommendations, by analyzing the resources tagged by a user and the frequency of each used tag. Experimental results highlight that, by analyzing both the tagging and bookmarking behaviors of a user, our approach is able to mine preferences in a more accurate way with respect to a state-of-the-art approach that considers only the tags.


Social bookmarking Friend recommendation Behavioral data mining Tagging system 


  1. 1.
    Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 19–26. ACM, New York (2006).
  2. 2.
    Arru, G., Gurini, D. F., Gasparetti, F., Micarelli, A., Sansonetti, G.: Signal-based user recommendation on twitter. In: Carr, L., Laender, A.H.F., Lóscio, B.F., King, I., Fontoura, M., Vrandecic, D., Aroyo, L., de Oliveira, J.P.M., Lima, F., Wilde, E. (eds.) 22nd International World Wide Web Conference, WWW 2013, 13–17 May 2013, Rio de Janeiro, Brazil, Companion volume, pp. 941–944. International World Wide Web Conferences Steering Committee/ACM (2013)Google Scholar
  3. 3.
    Barbieri, N., Bonchi, F., Manco, G.: Who to follow and why: link prediction with explanations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 1266–1275. ACM, New York (2014).
  4. 4.
    Boratto, L., Carta, S.: State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Soro, A., Vargiu, E., Armano, G., Paddeu, G. (eds.) Information Retrieval and Mining in Distributed Environments. SCI, vol. 324, pp. 1–20. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  5. 5.
    Boratto, L., Carta, S.: Impact of content novelty on the accuracy of a group recommender system. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 159–170. Springer, Heidelberg (2014). Google Scholar
  6. 6.
    Boratto, L., Carta, S.: Modeling the preferences of a group of users detected by clustering: a group recommendation case-study. In: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14), WIMS 2014, pp. 16:1–16:7. ACM, New York (2014).
  7. 7.
    Boratto, L., Carta, S.: The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation. J. Intell. Inf. Syst., 1–25 (2014).
  8. 8.
    Boratto, L., Carta, S.: Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system. In: Proceedings of 16th International Conference on Enterprise Information Systems (ICEIS), pp. 564–572 (2014)Google Scholar
  9. 9.
    Boratto, L., Carta, S., Chessa, A., Agelli, M., Clemente, M. L.: Group recommendation with automatic identification of users communities. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2009, vol. 03, pp. 547–550. IEEE Computer Society, Washington, DC (2009).
  10. 10.
    Boratto, L., Carta, S., Manca, M., Mulas, F., Pilloni, P., Pinna, G., Vargiu, E.: A clustering approach for tag recommendation in social environments. Int. J. E Bus. Dev. 3, 126–136 (2013)Google Scholar
  11. 11.
    Boratto, L., Carta, S., Satta, M.: Groups identification and individual recommendations in group recommendation algorithms. In: Picault, J., Kostadinov, D., Castells, P., Jaimes, A. (eds.) Practical Use of Recommender Systems, Algorithms and Technologies 2010. CEUR Workshop Proceedings, vol. 676, November 2010.
  12. 12.
    Boratto, L., Carta, S., Vargiu, E.: RATC: a robust automated tag clustering technique. In: Di Noia, T., Buccafurri, F. (eds.) EC-Web 2009. LNCS, vol. 5692, pp. 324–335. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  13. 13.
    Boyd, D.M., Ellison, N.B.: Social network sites: definition, history, and scholarship. J. Comput. Mediated Commun. 13(1), 210–230 (2007)CrossRefGoogle Scholar
  14. 14.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, UAI 1998, pp. 43–52. Morgan Kaufmann Publishers Inc., San Francisco (1998).
  15. 15.
    Brzozowski, M.J., Romero, D.M.: Who should i follow? recommending people in directed social networks. In: Adamic, L.A., Baeza-Yates, R.A., Counts, S. (eds.) Proceedings of the Fifth International Conference on Weblogs and Social Media, 17-21 July 2011. The AAAI Press, Barcelona (2011)Google Scholar
  16. 16.
    Buckland, M., Gey, F.: The relationship between recall and precision. J. Am. Soc. Inf. Sci. 45(1), 12–19 (1994).<12:AID-ASI2>3.0.CO;2-L
  17. 17.
    Cantador, I., Brusilovsky, P., Kuflik, T.: Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In: Mobasher, B., Burke, R.D., Jannach, D., Adomavicius, G. (eds.) Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, 23–27 October 2011, Chicago, IL, USA, pp. 387–388. ACM (2011)Google Scholar
  18. 18.
    Chen, J., Geyer, W., Dugan, C., Muller, M.J., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: Olsen, Jr., D.R., Arthur, R.B., Hinckley, K., Morris, M.R., Hudson, S.E., Greenberg, S. (eds.) Proceedings of the 27th International Conference on Human Factors in Computing Systems, CHI 2009, 4–9 April 2009, Boston, MA, USA, pp. 201–210. ACM (2009)Google Scholar
  19. 19.
    Farooq, U., Kannampallil, T.G., Song, Y., Ganoe, C.H., Carroll, J.M., Giles, C.L.: Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics. In: Gross, T., Inkpen, K. (eds.) Proceedings of the 2007 International ACM SIGGROUP Conference on Supporting Group Work, GROUP 2007, 4–7 November 2007, Sanibel Island, Florida, USA, pp. 351–360. ACM (2007)Google Scholar
  20. 20.
    Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., Zadeh, R.: Wtf: the who to follow service at twitter. In: Schwabe, D., Almeida, V.A.F., Glaser, H., Baeza-Yates, R.A., Moon, S.B. (eds.) 22nd International World Wide Web Conference, WWW 2013, 13–17 May 2013, Rio de Janeiro, Brazil, pp. 505–514. International World Wide Web Conferences Steering Committee/ACM (2013)Google Scholar
  21. 21.
    Guy, I., Carmel, D.: Social recommender systems. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011 (Companion volume), pp. 283–284. ACM (2011)Google Scholar
  22. 22.
    Guy, I., Chen, L., Zhou, M.X.: Introduction to the special section on social recommender systems. ACM TIST 4(1), 7 (2013)Google Scholar
  23. 23.
    Guy, I., Ronen, I., Wilcox, E.: Do you know?: recommending people to invite into your social network. In: Conati, C., Bauer, M., Oliver, N., Weld, D.S. (eds.) Proceedings of the 2009 International Conference on Intelligent User Interfaces, 8–11 February 2009, Sanibel Island, Florida, USA, pp. 77–86. ACM (2009)Google Scholar
  24. 24.
    Hannon, J., Bennett, M., Smyth, B.: Recommending twitter users to follow using content and collaborative filtering approaches. In: Amatriain, X., Torrens, M., Resnick, P., Zanker, M. (eds.) Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, 26–30 September 2010, Barcelona, Spain, pp. 199–206. ACM (2010)Google Scholar
  25. 25.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 15–19 August 1999, Berkeley, CA, USA, pp. 230–237. ACM (1999)Google Scholar
  26. 26.
    Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: Proceedings of the 2003 ACM CIKM International Conference on Information and Knowledge Management, 2–8 November 2003, New Orleans, Louisiana, USA, pp. 556–559. ACM (2003)Google Scholar
  27. 27.
    Manca, M., Boratto, L., Carta, S.: Design and architecture of a friend recommender system in the social bookmarking domain. In: Proceedings of the Science and Information Conference 2014, pp. 838–842 (2014)Google Scholar
  28. 28.
    Manca, M., Boratto, L., Carta, S.: Mining user behavior in a social bookmarking system - A delicious friend recommender system. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds.) DATA 2014 - Proceedings of 3rd International Conference on Data Management Technologies and Applications, Vienna, Austria, 29–31 August, 2014. pp. 331–338. SciTePress (2014)Google Scholar
  29. 29.
    Marlow, C., Naaman, M., Boyd, D., Davis, M.: Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, HYPERTEXT 2006, pp. 31–40. ACM, New York (2006).
  30. 30.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43(8), 142–151 (2000). CrossRefGoogle Scholar
  31. 31.
    Pearson, K.: Mathematical contributions to the theory of evolution. iii. Regression, heredity and panmixia, Philosophical transactions of the royal society of London. In: Series A, Containing Papers of a Math. or Phys. Character (1896–1934), vol. 187, pp. 253–318, January 1896Google Scholar
  32. 32.
    Quercia, D., Capra, L.: Friendsensing: recommending friends using mobile phones. In: Bergman, L.D., Tuzhilin, A., Burke, R.D., Felfernig, A., Schmidt-Thieme, L. (eds.) Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, 23–25 October 2009, pp. 273–276. ACM, New York (2009)Google Scholar
  33. 33.
    Ratiu, F.: Facebook: People you may know, May 2008.
  34. 34.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, USA (2011)CrossRefGoogle Scholar
  35. 35.
    Simon, H.A.: Designing organizations for an information rich world. In: Greenberger, M. (ed.) Computers, communications, and the public interest, pp. 37–72. Johns Hopkins Press, Baltimore (1971)Google Scholar
  36. 36.
    Xiong, H., Shekhar, S., Tan, P.N., Kumar, V.: Exploiting a support-based upper bound of pearson’s correlation coefficient for efficiently identifying strongly correlated pairs. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 334–343. ACM, New York (2004).
  37. 37.
    Zhou, T.C., Ma, H., Lyu, M.R., King, I.: Userrec: a user recommendation framework in social tagging systems. In: Fox, M., Poole, D. (eds.) Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, 11–15 July 2010, Atlanta, Georgia, USA. AAAI Press (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Matteo Manca
    • 1
  • Ludovico Boratto
    • 1
  • Salvatore Carta
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di CagliariCagliariItaly

Personalised recommendations