Toward a Context-Aware Multilingual Personalized Search

  • Mohamed Seghir Hadj Ameur
  • Youcef Moulahoum
  • Lamia Berkani
  • Ahmed Guessoum
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 782)


In recent years, personalized search has widely been used in Information Retrieval Systems (IRS) to provide the end user with more sophisticated and accurate search results. A basic element that plays an important role in personalized search is the user context which contains several aspects such as the user preferences, navigation history, habits, etc. A user may express his information needs in various languages. This requires the IRS to be able to consider all the contextual information provided in these languages. In this work, we present M-CAIRS, a Multilingual Context-aware Information Retrieval System that takes into account multilingual user contexts to better model the user search interests. Experimental results show a strong correlation between the user’s relevance judgment and the automatic results obtained by our system, which proves the consistency and adequacy of our proposal.


Information retrieval Multilingual information retrieval Reference ontology Document indexing User context User profile Relevance judgment 


  1. 1.
    Stokoe, C., Oakes, M.P., Tait, J.: Word sense disambiguation in information retrieval revisited. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 159–166. ACM (2003)Google Scholar
  2. 2.
    Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive web search based on user profile constructed without any effort from users. In: Proceedings of the 13th International Conference on World Wide Web, pp. 675–684. ACM (2004)Google Scholar
  3. 3.
    Teevan, J., Morris, M.R., Bush, S.: Discovering and using groups to improve personalized search. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pp. 15–24. ACM (2009)Google Scholar
  4. 4.
    Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 449–456. ACM (2005)Google Scholar
  5. 5.
    Speretta, M., Gauch, S.: Personalized search based on user search histories. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 622–628. IEEE (2005)Google Scholar
  6. 6.
    Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: Proceedings of the 15th International Conference on World Wide Web, pp. 727–736. ACM (2006)Google Scholar
  7. 7.
    White, R.W., Chu, W., Hassan, A., He, X., Song, Y., Wang, H.: Enhancing personalized search by mining and modeling task behavior. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1411–1420. ACM (2013)Google Scholar
  8. 8.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  9. 9.
    Berkovsky, S., Kuflik, T., Ricci, F.: Mediation of user models for enhanced personalization in recommender systems. User Model. User-Adapted Interact. 18(3), 245–286 (2008)CrossRefGoogle Scholar
  10. 10.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 191–226. Springer, Boston (2015). CrossRefGoogle Scholar
  11. 11.
    Henze, N., Dolog, P., Nejdl, W., et al.: Reasoning and ontologies for personalized e-learning in the semantic web. Educ. Tech. Soc. 7(4), 82–97 (2004)Google Scholar
  12. 12.
    Chen, C.M., Lee, H.M., Chen, Y.H.: Personalized e-learning system using item response theory. Comput. Educ. 44(3), 237–255 (2005)CrossRefGoogle Scholar
  13. 13.
    Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 525–534. ACM (2007)Google Scholar
  14. 14.
    Gupta, K., Arora, A.: Web search personalization using ontological user profiles. In: Babu, B.V., Nagar, A., Deep, K., Pant, M., Bansal, J.C., Ray, K., Gupta, U. (eds.) SocProS 2012. AISC, vol. 236, pp. 849–855. Springer, New Delhi (2014). Google Scholar
  15. 15.
    Gauch, S., Speretta, M., Pretschner, A.: Ontology-based user profiles for personalized search. In: Sharman, R., Kishore, R., Ramesh, R. (eds.) Ontologies. ISIS, vol. 14, pp. 665–694. Springer, Boston (2007). CrossRefGoogle Scholar
  16. 16.
    Pazzani, M.J., Muramatsu, J., Billsus, D., et al.: Syskill & webert: identifying interesting web sites. In: AAAI/IAAI, vol. 1, pp. 54–61 (1996)Google Scholar
  17. 17.
    Joachims, T.: Optimizing search engines using click through data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2002)Google Scholar
  18. 18.
    Jay, P., Shah, P., Makvana, K., Shah, P.: An approach to identify user interest by reranking personalize web. In: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, p. 64. ACM (2016)Google Scholar
  19. 19.
    Lovaraju, D., Devi, G.L.: An ontology like model for gathering personalized web information. Int. J. Adv. Res. Comput. Sci. 8(3), 401–403 (2017)Google Scholar
  20. 20.
    Vicente-López, E., de Campos, L.M., Fernández-Luna, J.M., Huete, J.F.: Use of textual and conceptual profiles for personalized retrieval of political documents. Knowl.-Based Syst. 112, 127–141 (2016)CrossRefGoogle Scholar
  21. 21.
    Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  22. 22.
    Moukas, A.: Amalthaea information discovery and filtering using a multiagent evolving ecosystem. Appl. Artif. Intell. 11(5), 437–457 (1997)CrossRefGoogle Scholar
  23. 23.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefzbMATHGoogle Scholar
  24. 24.
    Safi, H., Jaoua, M., Belguith, L.H.: PIRAT: a personalized information retrieval system in arabic texts based on a hybrid representation of a user profile. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds.) NLDB 2016. LNCS, vol. 9612, pp. 326–334. Springer, Cham (2016). CrossRefGoogle Scholar
  25. 25.
    Houssem, S., Maher, J., Lamia, B.H.: Axon: a personalized retrieval information system in Arabic texts based on linguistic features. In: 2015 6th International Conference on Information Systems and Economic Intelligence (SIIE), pp. 165–172. IEEE (2015)Google Scholar
  26. 26.
    Black, W., Elkateb, S., Rodriguez, H., Alkhalifa, M., Vossen, P., Pease, A., Fellbaum, C.: Introducing the Arabic wordnet project. In: Proceedings of the Third International WordNet Conference, pp. 295–300 (2006)Google Scholar
  27. 27.
    Google Corporation: Google language detection API. Accessed 2017
  28. 28.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)Google Scholar
  29. 29.
    Daoud, M., Tamine, L., Boughanem, M.: A personalized graph-based document ranking model using a semantic user profile. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 171–182. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  30. 30.
    Hawking, D., Craswell, N., Bailey, P., Griffihs, K.: Measuring search engine quality. Inf. Retriev. 4(1), 33–59 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Laboratory for Research in Artificial Intelligence (LRIA), NLP, Machine Learning and Applications (TALAA) Group, Department of Computer ScienceUniversity of Science and Technology Houari Boumediene (USTHB)Bab-Ezzouar, AlgiersAlgeria

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