Mining User Behavior in a Social Bookmarking System - A Delicious Friend Recommender System

Matteo Manca, Ludovico Boratto, Salvatore Carta

2014

Abstract

The growth of the Web 2.0 has brought to a widespread use of social media systems. In particular, social bookmarking systems are a form of social media system that allows to tag bookmarks of interest for a user and to share them. The increasing popularity of these systems leads to an increasing number of active users and this implies that each user interacts with too many users ("social interaction overload"). In order to overcome this problem, we present a friend recommender system in the social bookmarking domain. Recommendations are produced by mining user behavior in a tagging system, analyzing the bookmarks tagged by a user and the frequency of each used tag. Experimental results highlight that, by analyzing both the tagging and bookmarking behavior of a user, our approach is able to mine preferences in a more accurate way, with respect to state-of-the-art approaches that consider only tags.

References

  1. Arru, G., Gurini, D. F., Gasparetti, F., Micarelli, A., and Sansonetti, G. (2013). Signal-based user recommendation on twitter. In 22nd International World Wide Web Conference, WWW 7813, Companion Volume, pages 941-944. International World Wide Web Conferences Steering Committee / ACM.
  2. Boyd, D. M. and Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. J. ComputerMediated Communication, 13(1):210-230.
  3. Breese, J. S., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, UAI'98, pages 43-52, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  4. Brzozowski, M. J. and Romero, D. M. (2011). Who should i follow? recommending people in directed social networks. In Proceedings of the Fifth International Conference on Weblogs and Social Media. The AAAI Press.
  5. Buckland, M. and Gey, F. (1994). The relationship between recall and precision. J. Am. Soc. Inf. Sci., 45(1):12-19.
  6. Cantador, I., Brusilovsky, P., and Kuflik, T. (2011). Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, pages 387-388. ACM.
  7. Chen, J., Geyer, W., Dugan, C., Muller, M. J., and Guy, I. (2009). Make new friends, but keep the old: recommending people on social networking sites. In Proceedings of the 27th International Conference on Human Factors in Computing Systems, CHI 2009, pages 201-210. ACM.
  8. Farooq, U., Kannampallil, T. G., Song, Y., Ganoe, C. H., Carroll, J. M., and Giles, C. L. (2007). Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics. In Proceedings of the 2007 International ACM SIGGROUP Conference on Supporting Group Work, GROUP 2007, pages 351-360. ACM.
  9. Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., and Zadeh, R. (2013). Wtf: the who to follow service at twitter. In 22nd International World Wide Web Conference, WWW 7813, pages 505-514. International World Wide Web Conferences Steering Committee / ACM.
  10. Guy, I. and Carmel, D. (2011). Social recommender systems. In Proceedings of the 20th International Conference on World Wide Web, WWW 2011 (Companion Volume), pages 283-284. ACM.
  11. Guy, I., Chen, L., and Zhou, M. X. (2013). Introduction to the special section on social recommender systems. ACM TIST, 4(1):7.
  12. Guy, I., Ronen, I., and Wilcox, E. (2009). Do you know?: recommending people to invite into your social network. In Proceedings of the 2009 International Conference on Intelligent User Interfaces, pages 77-86. ACM.
  13. Hannon, J., Bennett, M., and Smyth, B. (2010). Recommending twitter users to follow using content and collaborative filtering approaches. In Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, pages 199-206. ACM.
  14. Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In SIGIR 7899: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230-237. ACM.
  15. Liben-Nowell, D. and Kleinberg, J. M. (2003). The link prediction problem for social networks. In Proceedings of the 2003 ACM CIKM International Conference on Information and Knowledge Management, pages 556- 559. ACM.
  16. Pearson, K. (1896). Mathematical contributions to the theory of evolution. iii. regression, heredity and panmixia. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Math. or Phys. Character (1896-1934), 187:253-318.
  17. Quercia, D. and Capra, L. (2009). Friendsensing: recommending friends using mobile phones. In Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys 2009, pages 273-276. ACM.
  18. Ratiu, F. (2008). Facebook: People you may know.
  19. Ricci, F., Rokach, L., and Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender Systems Handbook, pages 1-35. Springer.
  20. Simon, H. A. (1971). Designing organizations for an information rich world. In Computers, communications, and the public interest, pages 37-72. Johns Hopkins Press, Baltimore.
  21. Zhou, T. C., Ma, H., Lyu, M. R., and King, I. (2010). Userrec: A user recommendation framework in social tagging systems. In Proceedings of the TwentyFourth AAAI Conference on Artificial Intelligence, AAAI 2010. AAAI Press.
Download


Paper Citation


in Harvard Style

Manca M., Boratto L. and Carta S. (2014). Mining User Behavior in a Social Bookmarking System - A Delicious Friend Recommender System . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 331-338. DOI: 10.5220/0005000203310338


in Bibtex Style

@conference{data14,
author={Matteo Manca and Ludovico Boratto and Salvatore Carta},
title={Mining User Behavior in a Social Bookmarking System - A Delicious Friend Recommender System},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={331-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005000203310338},
isbn={978-989-758-035-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Mining User Behavior in a Social Bookmarking System - A Delicious Friend Recommender System
SN - 978-989-758-035-2
AU - Manca M.
AU - Boratto L.
AU - Carta S.
PY - 2014
SP - 331
EP - 338
DO - 10.5220/0005000203310338