A Semantic Method to Extract the User Interest Center

  • Ibtissam El AchkarEmail author
  • Amine Labriji
  • Labriji El Houssine
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)


With the evolution of information systems and the large mass of heterogeneous data on the web, problems such as cognitive overload and disorientation (Conklin and Begeman in GIBIS a hypertext tool for team design deliberation, pp. 247–251, [1]) are starting to appear, and to overcome them several techniques have been invented to customize the information system, in order to improve search engine performance as well as recommendation engines. (Zeng et al. in Temporal User Profile Based Recommender System. Artificial Intelligence and Soft Computing. ICAISC 2018, [2], Su et al. in Music Recommendation Based on Information of User Profiles, Music Genres and User Ratings. Intelligent Information and Database Systems. ACIIDS 2018, [3]) have shown that the best techniques to provide relevant results to the specific needs of the user are based on the use of user profiles and more specifically its interests. Generally the selection of interesting documents to a user is done on the basis of his area of interest, inferred from the information about the user or his user profile. Thus the calculation of the interest center is one of the essential elements for a relevant research. In this article we will introduce a new method of extracting the user’s interests from his knowledge, based on the structure of an ontology to deduce the user’s interest’s center, taking into account the semantic links between the graph’s concepts.


User profile Interest center Semantic web Recommender systems Adaptive information systems Ontology Similarity measurement Conceptual graph 


  1. 1.
    Conklin, J., Begeman, M.L.: GIBIS A Hypertext Tool for Team Design Deliberation, pp. 247–251 (1987)Google Scholar
  2. 2.
    Zeng, W., Du, Y., Zhang, D., Ye, Z., Dou, Z.: Temporal user profile based recommender system. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds.) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science, vol 10842. Springer (2018)Google Scholar
  3. 3.
    Su., J.H., Chin, C.Y., Yang, H.C., Tseng, V.S., Hsieh. S.Y.: Music recommendation based on information of user profiles, music genres and user ratings. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds.) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science, vol 10751. Springer (2018)Google Scholar
  4. 4.
    Bradford, C., Marshall, I.W.: A bandwidth friendly search engine. Proc. IEEE Int. Conf. Multimed. Comput. Syst. 2, 720–724 (1999)CrossRefGoogle Scholar
  5. 5.
    Gasparetti, F.: Modeling user interests from web browsing activities. Data Min. Knowl. Discov. 31(2), 502547 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Al-Qurishi, M., Alhuzami, S., AlRubaian, M. et al. User profiling for big social media data using standing ovation model. Multime. Tools Appl. 77(9), 11179–11201 (2018). SpringerGoogle Scholar
  7. 7.
    Zhang, L., Fu, S., Jiang, S., Bao, R., Zeng Y. (2018) A fusion model of multi-data sources for user profiling in social media. In: Natural Language Processing and Chinese Computing. NLPCC 2018, vol. 11109. SpringerGoogle Scholar
  8. 8.
    Frikha, M., Mhiri, M., Gargouri, F.: Using social interaction between friends in knowledge-based personalized recommendation. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), IEEE, Tunisia (2018)Google Scholar
  9. 9.
    Hassan, O., Habegger, B., Brunie, L., Bennani, N., Damiani, E.: A discussion of privacy challenges in user profiling with big data techniques: the EEXCESS use case. In: 2013 IEEE International Congress on Big Data, pp. 25–30 (2013)Google Scholar
  10. 10.
    Gao, M., Liu, K., Wu Z.: Personalisation in web computing and informatics: theories, techniques, applications, and future research. Inf. Syst. Front. 607–629 (2010)CrossRefGoogle Scholar
  11. 11.
    Frias-Martinez, E., Magoulas, G., Chen, S., Macredie, R.: Automated user modeling for personalized digital. Int. J. Inf. Manag. 234–248 (2006)Google Scholar
  12. 12.
    Sarukkai, Link prediction and path analysis using Markov chains. Comput. Netw. 33, 377–386, (2000)CrossRefGoogle Scholar
  13. 13.
    Jung, S.Y., Hong, J.H., Kim, T.S.: A statistical model for user preference. IEEE Trans. Knowl. Data Eng. 834–843 (2005)Google Scholar
  14. 14.
    Schubert, P., Koch, M.: The power of personalization: customer collaboration and virtual communities. In: Proceedings of the Eighth Americas Conference on Information Systems (AMCIS), pp. 1953–1965 (2002)Google Scholar
  15. 15.
    Freitag, D., Joachims, T., Mitchell, T., Armstrong, R.: WebWatcher: a learning apprentice for the World Wide Web. In: Proceedings of the 1995 AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, March (1995)Google Scholar
  16. 16.
    Tebri, H., Boughanem, M., Chrisment, C., Tmar, M.: Incremental profile learning based on a reinforcement method. In: SAC’2005-20th ACM Symposium on Applied Computing, Santa Fe, New Mexico, USA, pp. 1096–1101, mars (2005)Google Scholar
  17. 17.
    Pazzani, M., Muramatsu, J., Billsus, D.: Syskill Webert: identifying interesting web sites. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence (1996)Google Scholar
  18. 18.
    Kieling, W., Endres, M., Preisinger, T.: The BNL ++ Algorithm for Evaluating Pareto Preference Queries (2006)Google Scholar
  19. 19.
    Salton, G., Yang, C.S.: On the specification of terms values in automatic indexing. J. Doc. 29, 351–372 (1973)CrossRefGoogle Scholar
  20. 20.
    Huhns, M.N., Stevens, L.M.: Personal ontologies. IEEE Internet Comput. 3, 85–87 (1999)CrossRefGoogle Scholar
  21. 21.
    Chaffee, J., GAUCH, S.: Personal ontologies for web navigation. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, CIKM, 2000Google Scholar
  22. 22.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)CrossRefGoogle Scholar
  23. 23.
    Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. J. Knowl. Eng. Rev. 18, 95–145 (2003)CrossRefGoogle Scholar
  24. 24.
    Zemirli, N., Boughanem, M., Tamine-Lechani, L.: Exploiting multi-evidence from multiple user’s interests to personalizing information retrieval. In: IEEE 2nd International Conference on Digital Information Management (ICDIM), France (2008)Google Scholar
  25. 25.
    Speretta, M., Gauch, S.: Personalized search based on user search histories. Web Intell. 622–628 (2005)Google Scholar
  26. 26.
    Daoud, M., Tamine, L., Boughanem, M.: Towards a graph based user profile modeling for a session-based. Knowl. Inf. Syst. 21(3), 365–398 (2009)CrossRefGoogle Scholar
  27. 27.
    Rami Ghorab, M., Zhou, D., OConnor, A., Wade, V.: Personalised information retrieval: survey and classification. User Model. User-Adapt. Interact. 23(4), 381443 (2013)CrossRefGoogle Scholar
  28. 28.
    Pretschner, A., Gauch, S.: Ontology based personalized search. In: Proceedings 11th International Conference on Tools with Artificial Intelligence, Chicago (2002)Google Scholar
  29. 29.
    Tanudjaja F., Mui, L.: Persona: a contextualized and personalized web search. In: Proceedings of the 35th Hawaii International Conference on System Sciences (2002)Google Scholar
  30. 30.
    Salton, G., Yang, S.C.: The specification of terms values in automatic indexing. J. Doc. 29(4), 351–372 (1973)CrossRefGoogle Scholar
  31. 31.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proceedings of the 32 nd Annual Meeting of the Associations for Computational Linguistics, pp. 133–138 (1994)Google Scholar
  32. 32.
    Labriji, A., Abdelbaki, I., Reddahi, N., Namir, A., Boudou, A.: Conceptual similarity measure. J. Theor. Appl. Inf. Technol. 83(2), 291–298 (2016)Google Scholar
  33. 33.
    Siriaraya, P., Yamaguchi, Y., Morishita, M.: Using categorized web browsing history to estimate the user’s latent interests for web advertisement recommendation. In: 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA (2018)Google Scholar
  34. 34.
    Lv, J.: User interest degree evaluation models. In: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China (2018)Google Scholar
  35. 35.
    Ko, H.G., Ko I.Y., Kim T., Lee D., Hyun S.J.: Identifying user interests from online social networks by using semantic clusters generated from linked data. In: Sheng Q.Z., Kjeldskov J. (eds.) Current Trends in Web Engineering. ICWE 2013. Lecture Notes in Computer Science, vol 8295. Springer, Cham (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ibtissam El Achkar
    • 1
    Email author
  • Amine Labriji
    • 1
  • Labriji El Houssine
    • 1
  1. 1.Laboratory of Technological Information and Modeling, Faculty of Sciences Ben M’sickUniversity Hassan IICasablancaMorocco

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