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Topology-Based Recommendation of Users in Micro-Blogging Communities

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Nowadays, more and more users share real-time news and information in micro-blogging communities such as Twitter, Tumblr or Plurk. In these sites, information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows, named followees. With the increasing number of registered users in this kind of sites, finding relevant and reliable sources of information becomes essential. The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation. To address this problem, we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources. Experimental evaluation conducted with a group of users is reported, demonstrating the potential of the approach.

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  1. [1]

    Java A, Song X, Finin T, Tseng B. Why we twitter: Understanding microblogging usage and communities. In Proc. WebKDD/SNA-KDD, Aug. 2007, pp.56–65.

  2. [2]

    Krishnamurthy B, Gill P, Arlitt M. A few chirps about twitter. In Proc. WOSP, Aug. 2008, pp.19–24.

  3. [3]

    Weng J, Lim E P, Jiang J, He Q. TwitterRank: Finding topic-sensitive influential twitterers. In Proc. WSDM, Feb. 2010, pp.261–270.

  4. [4]

    Yamaguchi Y, Takahashi T, Amagasa T, Kitagawa H. TU-Rank: Twitter user ranking based on user-tweet graph analysis. In Proc. the 11th Int. Conf. Web In formation Systems Engineering, Dec. 2010, pp.240–253.

  5. [5]

    Chen J, Nairn R, Nelson L, Bernstein M, Chi E. Short and tweet: Experiments on recommending content from information streams. In Proc. CHI, April 2010, pp.1185–1194.

  6. [6]

    Phelan O, McCarthy K, Smyth B. Using twitter to recommend real-time topical news. In Proc. RecSys, 2009, pp.385–388.

  7. [7]

    Esparza S G, O'Mahony M P, Smyth B. On the real-time web as a source of recommendation knowledge. In Proc. RecSys, Sept. 2010, pp.305–308.

  8. [8]

    Hannon J, Bennett M, Smyth B. Recommending twitter users to follow using content and collaborative filtering approaches. In Proc. RecSys, Sept. 2010, pp.199–206.

  9. [9]

    Guy I, Ronen I, Wilcox E. Do you know?: Recommending people to invite into your social network. In Proc. IUI, Feb. 2009, pp.77–86.

  10. [10]

    Liben-Nowell D, Kleinberg J. The link prediction problem for social networks. In Proc. CIKM, Nov. 2003, pp.556–559.

  11. [11]

    Chen J, Geyer W, Dugan C, Muller M, Guy I. Make new friends, but keep the old: Recommending people on social networking sites. In Proc. CHI, April 2010, pp.201–210.

  12. [12]

    Lo S, Lin C. WMR|A graph-based algorithm for friend recommendation. In Proc. WI, Dec. 2006, pp.121–128.

  13. [13]

    Kwak H, Lee C, Park H, Moon S. What is Twitter, a social network or a news media? In Proc. WWW, April 2010, pp.591–600.

  14. [14]

    Cha M, Haddadi H, Benevenuto F, Gummadi K P. Measuring user influence in Twitter: The million follower fallacy. In Proc. ICWSM, May 2010.

  15. [15]

    Garcia R, Amatriain X. Weighted content based methods for recommending connections in online social networks. In Proc. the 2nd Workshop on Recommender Systems and the Social Web, Oct. 2010, pp.68–71.

  16. [16]

    Abel F, Gao Q, Houben G J, Tao K. Analyzing user modeling on twitter for personalized news recommendations. In Proc. UMAP, July 2011, pp.1–12.

  17. [17]

    Sun A R, Cheng J, Zeng D D. A novel recommendation frame-work for micro-blogging based on information diffusion. In Proc. Workshop on Information Technologies and Systems, Dec. 2009.

  18. [18]

    Phelan O, McCarthy K, Bennett M, Smyth B. Terms of a feather: Content-based news recommendation and discovery using twitter. In Proc. ECIR, April 2011, pp.448–459.

  19. [19]

    Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 1998, 30(1): 107–117.

  20. [20]

    Joachims T, Granka L, Pan B, Hembrooke H, Gay G. Accurately interpreting click through data as implicit feedback. In Proc. SIGIR, Aug. 2005, pp.154–161.

  21. [21]

    Deshpande M, Karypis G. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems, 2004, 22(1): 143–177.

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Author information

Correspondence to Marcelo G. Armentano.

Additional information

This research was partially supported by the National Scientific and Technical Research Council (CONICET) of Argentina under Grant PIP No. 114-200901-00381.

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Armentano, M.G., Godoy, D. & Amandi, A. Topology-Based Recommendation of Users in Micro-Blogging Communities. J. Comput. Sci. Technol. 27, 624–634 (2012).

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  • social networking
  • social technology
  • recommender system