Implicit Social Networks for Social Recommendation of Scholarly Papers

  • Shaikhah AlotaibiEmail author
  • Julita Vassileva
Part of the Studies in Big Data book series (SBD, volume 27)


Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. However, all approaches in the domain of research paper recommendation have used explicit social relations that are initiated by users. Moreover, the results of previous studies have shown that the recommendations produced cannot compete with traditional collaborative filtering. We argue that the available data in social bookmarking websites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking websites such as CiteULike and Mendeley, and propose three different implicit social networks to recommend relevant papers/people to users. We showed that the proposed implicit social networks connect users with similar interests and the relations are propagated through the networks. In addition, we showed that implicit social networks connect more users than the two of well-known explicit social networks (co-authorship and friendship).


Social network Implicit social network Hybrid recommendation Paper recommendation Social bookmarking websites Collaborative filtering 


  1. 1.
    Basu C, Hirsh H, Cohen WW, Nevill-Manning C. Technical paper recommendation: a study in combining multiple information sources. J Artif Intell Res. 2001;14(1):231–52.Google Scholar
  2. 2.
    McNee SM, et al. On the recommending of citations for research papers. In: Proceedings of the 2002 ACM conference on computer-supported cooperative work, New York, NY, USA; 2002. p. 116–125.Google Scholar
  3. 3.
    Torres R, McNee SM, Abel M, Konstan JA, Riedl J. Enhancing digital libraries with TechLens+. In: Proceedings of the 4th ACM/IEEE-CS joint conference on digital libraries, New York, NY; 2004. p. 228–236.Google Scholar
  4. 4.
    Kautz H, Selman B, Shah M. Referral web: Combining social networks and collaborative filtering. Commun ACM. 1997;40(3):63–5.CrossRefGoogle Scholar
  5. 5.
    Tang J, Hu X, Liu H. Social recommendation: a review. Soc Netw Anal Min. 2013;3(4):1113–33.CrossRefGoogle Scholar
  6. 6.
    Golbeck J. Generating predictive movie recommendations from trust in social networks. In: Proceedings of 4th international conference on trust management, Berlin, Germany; 2006. p. 93–104.Google Scholar
  7. 7.
    Ma H, Yang H, Lyu MR, King I. SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of 17th ACM conference on information and knowledge management, New York, NY, USA; 2008. p. 931–940.Google Scholar
  8. 8.
    Liu F, Lee HJ. Use of social network information to enhance collaborative filtering performance. Expert Syst Appl. 2010;37(7):4772–8.CrossRefGoogle Scholar
  9. 9.
    Massa P, Avesani P. Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on recommender systems. New York, NY: ACM; 2007. p. 17–24.Google Scholar
  10. 10.
    Groh G, Ehmig C. Recommendations in taste related domains: collaborative filtering vs. social filtering. In: Proceedings of the 2007 International ACM conference on supporting group work. New York, NY: ACM; 2007. p. 127–136.Google Scholar
  11. 11.
    Yuan Q, Zhao S, Chen L, Liu Y, Ding S, Zhang X, Zheng W. Augmenting collaborative recommender by fusing explicit social relationships. In: ACM workshop on recommender systems and the social web. New York, NY: ACM; 2009. p. 49–56.Google Scholar
  12. 12.
    Bellogin A, Cantador I, Castells P. A study of heterogeneity in recommendations for a social music service. In: Proceedings of the 1st international workshop on information heterogeneity and fusion in recommender systems, New York, NY, USA; 2010. p. 1–8.Google Scholar
  13. 13.
    Avesani P, Massa P, Tiella R. A trust-enhanced recommender system application: moleskiing. In: Proceedings of the ACM symposium on applied computing (SAC’05). New York, NY: ACM; 2004. p. 1589–1593.Google Scholar
  14. 14.
    O’Donovan J, Smyth B, Trust in recommender systems. In: Proceedings of the tenth international conference on intelligent user interfaces (IUI). New York, NY: ACM; 2005. p. 167–174.Google Scholar
  15. 15.
    Esslimani I, Brun A, Boyer A. Enhancing collaborative filtering by frequent usage patterns. In: Applications of digital information and web technologies (ICADIWT 2008). Ostrava, Czech republic: IEEE computer society; 2008. p. 180–185.Google Scholar
  16. 16.
    Ogata H, Yano Y, Furugori N, Jin Q. Computer supported social networking for augmenting cooperation. Comput Supported Coop Work. 2001;10(2):189–209.CrossRefGoogle Scholar
  17. 17.
    Guy I, Zwerdling N, Carmel D, Ronen I, Uziel E, Yogev S, Ofek-Koifman S. Personalized recommendation of social software items based on social relations. In: Proceedings of the third ACM conference on recommender systems. New York, NY: ACM; 2009. p. 53–60.Google Scholar
  18. 18.
    Pera MS, Ng Y-K. A personalized recommendation system on scholarly publications. In: Proceedings of 20th ACM international conference on information and knowledge management, New York, NY, USA; 2011. p. 2133–2136.Google Scholar
  19. 19.
    Pera MS, Ng Y-K. Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles. J Intell Inf Syst. 2014;42(3):371–91.CrossRefGoogle Scholar
  20. 20.
    Lee DH, Brusilovsky P. Improving recommendations using watching networks in a social tagging system. In: Proceedings of 2011 iConference, New York, NY, USA; 2011. p. 33–39.Google Scholar
  21. 21.
    Lee DH, Brusilovsky P. Interest similarity of group members: the case study of CiteULike. In: Presented at WebSci10: extending the frontiers of society on-line, Raleigh, NC, USA, 2010.Google Scholar
  22. 22.
    Lee D. Personalized recommendations based on users’ information-centered social networks. University of Pittsburgh, Pittsburgh, PA, USA, 2013.Google Scholar
  23. 23.
    Liu B. Informational retrieval and web search. In: Web data mining: exploring hyperlinks, contents and usage data. New York, NY: Springer; 2007. p. 183–236.Google Scholar
  24. 24.
    Granovetter M. The strength of weak ties: a network theory revisited. Sociol Theory. 1982;1:105–30.Google Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of SaskatchewanSaskatoonCanada
  2. 2.University of SaskatchewanSaskatoonCanada

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