Social Activities Recommendation System for Students in Smart Campus

  • Sabrine Ben AbdrabbahEmail author
  • Raouia Ayachi
  • Nahla Ben Amor
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)


Smart campus is generally defined as an academic institution that uses the Information and Communication Technologies (ICT) to enhance the quality of students’ life through buildings infrastructure, the way of learning and social activities planning. A large aspect of smart campus solution requires an intelligent information system which should be able to identify students’ requirements and make accordingly specific recommendations. This paper addresses the question of how smart campus can organize and plan events and activities for students by exploiting ICT. The proposed solution is based on a group recommender system of students’ social activities allowing them to express their preferences via complete orders. A new aggregation method (so-called Avg-Pos) is proposed to produce the global preference ordering relative to the whole group members. Experimental study carried out on a real-world data obtained from students of Lille1 University (France) points out the promising results on the effectiveness of the proposed recommender system.


Smart campus Students’ social life Group recommendation Ranking aggregation methods 


  1. 1.
    Ben Abdrabbah, S., Ayachi, R., Ben Amor, N.: A dynamic community-based personalization for e-Government services. In: Proceedings of the 9th International Conference on Theory and Practice of Electronic Governance, pp. 258–265 (2016)Google Scholar
  2. 2.
    Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys 2010), pp. 119–126 (2010)Google Scholar
  3. 3.
    De Pessemier, T., Dooms, S., Martens, L.: An improved data aggregation strategy for group recommendations. In: Proceedings of the 7th ACM Conference on Recommender Systems (RecSys 2013), pp. 36–39 (2013)Google Scholar
  4. 4.
    Brun, A., Hamad, A., Buet, O., Boyer, A.: Towards preference relations in recommender systems. In: Proceedings of the ECML/PKDD Workshop on Preference Learning (2010)Google Scholar
  5. 5.
    Bydzovská, H.: Course enrollment recommender system. In: Proceedings of the 9th International Conference on Educational DatanMining, EDM 2016, pp. 312–317 (2016)Google Scholar
  6. 6.
    Ray, S., Sharma, A.: A collaborative filtering based approach for recommending elective courses. In: Information Intelligence, Systems, Technology and Management, vol. 141, no. 1, pp. 330–339. Springer, Heidelberg (2011)Google Scholar
  7. 7.
    Hlaing, H.H., Ko, K.T.: Location-based recommender system for mobile devices on University Campus. In: Proceedings of 2015 International Conference on Future Computational Technologies (ICFCT 2015), Singapore, 29–30 March, pp. 204–210 (2015)Google Scholar
  8. 8.
    Bouzeghoub, A., Do, N.K., Krug Wives, L.: Situation-aware adaptive recommendation to assist mobile users in a campus environment. In: AINA 2009: The IEEE 23rd International Conference on Advanced Information Networking and Applications, pp. 503–509 (2009)Google Scholar
  9. 9.
    Zhang, F.: A personalized time-sequence-based book recommendation algorithm for digital libraries. IEEE Access. 4, 2714–2720 (2016)CrossRefGoogle Scholar
  10. 10.
    Cary, D.: Estimating the margin of victory for instant-runoff voting. In: Proceedings of the Conference on Electronic Voting Technology/Workshop on Trustworthy Elections (2011)Google Scholar
  11. 11.
    Saari, D.G.: The Borda dictionary. Soc. Choice Welfare 7, 279–319 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Davenport, A., Kalagnanam, J.: A computational study of the Kemeny rule for preference aggregation. In: Proceedings of the 19th National Conference on Artificial Intelligence, pp. 697–702 (2004)Google Scholar
  13. 13.
    Young, H.P., Levenglick, A.: A consistent extension of condorcet’s election principle. SIAM J. Appl. Math. 35(2), 285–300 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, New York, NY, USA, pp. 613–622 (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Sabrine Ben Abdrabbah
    • 1
    Email author
  • Raouia Ayachi
    • 1
  • Nahla Ben Amor
    • 1
  1. 1.LARODECUniversité de Tunis, ISG TunisBardoTunisia

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