Perceiving Intellectual Style to Solve Privacy Problem in Collaborative Systems

  • Ossama EmbarakEmail author
  • Kholoud Saeed
  • Manal Ali
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


Privacy problem is a big challenge in collaborative systems. Such systems depend on users collected data to generate recommendations in their future visits. Site visitors give falsify information to avoid privacy disclosure; this leads to inefficient recommendations. In this paper, we address the privacy problem in collaborative systems; we suggested a new perceiving intellectual style to generate recommendations and avoiding users’ privacy issues. According to the suggested approach, we were able to provide two types of recommendations, the Intellectual Node Recommendation or the Intellectual Batch Recommendation. We evaluated both recommendation types by calculating levels of coverage and precision. We found that Intellectual Batch Recommendation achieved better performance comparing to the Intellectual Node Recommendation.


  1. 1.
    Hafshejani, Z.Y., Kaedi, M., Fatemi, A.: Improving sparsity and new user problems in collaborative filtering by clustering the personality factors. Electron. Commer. Res. 18(4), 813–836 (2018)CrossRefGoogle Scholar
  2. 2.
    Elmisery, A.M., Botvich, D.: An enhanced middleware for collaborative privacy in IPTV recommender services (2017). arXiv preprint arXiv:1711.07593
  3. 3.
    Sofos, J.T., Chow, L.M., Piepenbrink, D.J.: US Patent No. 9,172,482. US Patent and Trademark Office, Washington, DC (2015)Google Scholar
  4. 4.
    Friedman, A., Knijnenburg, B.P., Vanhecke, K., Martens, L., Berkovsky, S.: Privacy aspects of recommender systems. In: Recommender Systems Handbook, pp. 649–688. Springer, Boston (2015)CrossRefGoogle Scholar
  5. 5.
    David, S., Pinch, T.J.: Six degrees of reputation: the use and abuse of online review and recommendation systems (2005)Google Scholar
  6. 6.
    Hosea, D.F., Zimmerman, R.S., Rascon, A.P., Oddo, A.S., Thurston, N.: US Patent No. 7,979,880. US Patent and Trademark Office, Washington, DC (2011)Google Scholar
  7. 7.
    Sarwar, S., Hall, L.: Task based segmentation in personalising E-government services. In: Proceedings of the 31st British Computer Society Human Computer Interaction Conference, p. 9. BCS Learning & Development Ltd., July 2017Google Scholar
  8. 8.
    Harper, F.M., Xu, F., Kaur, H., Condiff, K., Chang, S., Terveen, L.: Putting users in control of their recommendations. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 3–10. ACM, September 2015Google Scholar
  9. 9.
    Embarak, O.H.: A method for solving the cold start problem in recommendation systems. In: International Conference on Innovations in Information Technology, pp. 238–243 (2011)Google Scholar
  10. 10.
    Zhao, X.W., Guo, Y., He, Y., Jiang, H., Wu, Y., Li, X.: We know what you want to buy: a demographic-based system for product recommendation on microblogs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1935–1944. ACM, August 2014Google Scholar
  11. 11.
    Beel, J., Gipp, B., Langer, S., Breitinger, C.: Paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)CrossRefGoogle Scholar
  12. 12.
    Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User Adap. Inter. 25(2), 99–154 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer SciencesHigher Colleges of TechnologyFujairahUAE

Personalised recommendations