Abstract
Social media systems allow users to share resources with the people connected to them. In order to handle the exponential growth of the content in these systems and of the amount of users that populate them, recommender systems have been introduced. As social media systems with different purposes arose, also different types of social recommender systems were developed in order to filter the specific information that each domain handles. A form of social media, known as social bookmarking system, allows to share bookmarks in a social network. A user adds as a friend or follows another user and receives updates on the bookmarks added by that user. In this paper, we present an analysis of the state-of-the-art on user recommendation in social environments and of the structure of a social bookmarking system, in order to derive design guidelines and an architecture of a friend recommender system in the social bookmarking domain. This study can be useful for future research, by highlighting the aspects that characterize this domain and the features that this type of recommender system has to offer.
This work is partially funded by Regione Sardegna under project SocialGlue, through PIA—Pacchetti Integrati di Agevolazione “Industria Artigianato e Servizi” (annualità 2010).
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Given that traditional techniques to manually categorize data cannot be applied in social environments [4] and that clustering techniques represent a good form to extract information for recommendation purposes [3], the resources could be clustered based on the tags used to classify them, in order to extract some meta-information about a group of resources related to a specific topic.
References
Arasu, A., Ganti, V., Kaushik, R.: Efficient exact set-similarity joins. In: Proceedings of the 32nd International Conference on Very Large Data Bases. pp. 918–929. VLDB’ 06, VLDB Endowment (2006). http://dl.acm.org/citation.cfm?id=1182635.1164206
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 ‘13, Rio de Janeiro, Brazil, 13–17 May 2013, Companion Volume. pp. 941–944. International World Wide Web Conferences Steering Committee/ACM (2013)
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)
Boratto, L., Carta, S., Vargiu, E.: Ratc: A robust automated tag clustering technique. In: Noia, T.D., Buccafurri, F. (eds.) e-Commerce and Web Technologies. In: 10th International Conference, EC-Web 2009, Linz, Austria, 1–4 Sept 2009. Proceedings of Lecture Notes in Computer Science, vol. 5692, pp. 324–335. Springer (2009)
Boyd, D.M., Ellison, N.B.: Social network sites: Definition, history, and scholarship. J. Comput.-Mediated Commun. 13(1), 210–230 (2007)
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. pp. 43–52. UAI’98, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998). http://dl.acm.org/citation.cfm?id=2074094.2074100
Broder, A.: On the resemblance and containment of documents. In: Proceedings of the Compression and Complexity of Sequences 1997. pp. 21 SEQUENCES ‘97, IEEE Computer Society, Washington, DC, USA (1997). http://dl.acm.org/citation.cfm?id=829502.830043
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, Barcelona, Catalonia, Spain, 17–21 July 2011. The AAAI Press (2011)
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, Chicago, IL, USA, 23–27 Oct 2011. pp. 387–388. ACM (2011)
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: Jr., DRO, 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, Boston, MA, USA, 4–9 April 2009. pp. 201–210. ACM (2009)
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, Sanibel Island, Florida, USA, 4–7 Nov 2007. pp. 351–360. ACM (2007)
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’13, Rio de Janeiro, Brazil, 13–17 May 2013. pp. 505–514. International World Wide Web Conferences Steering Committee/ACM (2013)
Guy, I., Chen, L., Zhou, M.X.: Introduction to the special section on social recommender systems. ACM TIST 4(1), 7 (2013)
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.) In: Proceedings of the 2009 International Conference on Intelligent User Interfaces, 8–11 Feb 2009, Sanibel Island, Florida, USA. pp. 77–86. ACM (2009)
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, Barcelona, Spain, 26–30 Sept 2010. pp. 199–206. ACM (2010)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Bibsonomy: a social bookmark and publication sharing system. In: Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures. pp. 87–102 (2006)
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, New Orleans, Louisiana, USA, 2–8 Nov 2003. pp. 556–559. ACM (2003)
Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer (2011)
Lund, B., Hammond, T., Hannay, T., Flack, M.: Social bookmarking tools (ii): a case study—connotea. D-Lib Magazine 11(4) (2005)
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)
Manca, M., Boratto, L., Carta, S.: Mining user behavior in a social bookmarking system—a delicious friend recommender system. In: Proceedings of the 3rd International Conference on Data Management Technologies and Applications (DATA 2014). pp. 331–338 (2014)
Pearson, K.: 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 (1896)
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, New York, 23–25 Oct 2009. pp. 273–276. ACM (2009)
Ratiu, F.: Facebook: people you may know (May 2008), https://blog.facebook.com/blog.php?post=15610312130
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, Berlin (2011)
Sen, S., Lam, S.K., Rashid, A.M., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F.M., Riedl, J.: Tagging, communities, vocabulary, evolution. In: Hinds, P.J., Martin, D. (eds.) Proceedings of the 2006 ACM Conference on Computer Supported Cooperative Work, CSCW 2006, Banff, Alberta, Canada, 4–8 Nov 2006. pp. 181–190. ACM (2006)
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)
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. pp. 334–343. KDD ‘04, ACM, New York, NY, USA (2004). http://doi.acm.org/10.1145/1014052.1014090
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, Atlanta, Georgia, USA, 11–15 July 2010. AAAI Press (2010)
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Manca, M., Boratto, L., Carta, S. (2015). Friend Recommendation in a Social Bookmarking System: Design and Architecture Guidelines. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_14
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