Adapting User’s Context to Understand Mobile Information Needs

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 285)

Abstract

The use of the user’s environmental and physical context can reveal important information to enhance Mobile Information Retrieval. However the typical mobile search process integrates all gathered information about the user’s context. These approaches do not take into account user’s intention behind the query, which decreases their reliability and effectiveness in terms of leading to the appropriate user’s information need. In this paper, we study the problem of finding a set of user’s context information allow to disambiguate user’s query. These contextual informations, that we call relevant dimensions, can help to personalize the mobile search process. To this aim we develop a context filtering approach CFA. The problem of finding such set of dimensions can be assimilated to a context filtering problem. We propose a novel measure that directly precises the relevance degree of each contextual dimension, which leads to finally filter the user’s context by retaining only relevant. Our experiments show that our measure can analyze the real user’s context of up to 6,000 of dimensions related to more than 2,000 of user’s queries. We also show experimentally the quality of the set of contextual dimensions proposed, and the interest of the measure to understand mobile user’s needs and to filter his context.

Keywords

Information retrieval Mobile information Relevance analysis Personalise web search 

References

  1. 1.
    Mario, A., Cantera, J.M., Fuente, P., Llamas, C., Vegas, J.: Knowledge-based thesaurus recommender system in mobile web search (2010)Google Scholar
  2. 2.
    Varma, V., Sriharsha, N., Pingali, P.: Personalized web search engine for mobile devices. In: International Workshop on Intelligent Information Access (2006)Google Scholar
  3. 3.
    Yau, S., Liu, H., Huang, D., Yao, Y.: Situation-aware personalized information retrieval for mobile internet. In: The 27th Annual International Computer Software and Applications Conference (2003)Google Scholar
  4. 4.
    Bouidghaghen, O.: Accés contextuel à l’information dans un environnement mobile : approche basée sur l’utilisation d’un profil situationnel de l’utilisateur et d’un profil de localisation des requêtes. Thesis of Paul Sabatier University (2011)Google Scholar
  5. 5.
    Welch, M., Cho, J.: Automatically identifying localizable queries. In: Proceedings of 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1185–1186 (2008)Google Scholar
  6. 6.
    Chirita, P., Firan, C., Nejdl, W.: Summarizing local context to personalize global Web search. In: Proceedings of the Annual International Conference on Information and Knowledge Management, pp. 287–296 (2006)Google Scholar
  7. 7.
    Vadrevu, S., Zhang, Y., Tseng, B., Sun, G., Li, X.: Identifying regional sensitive queries in web search. In: WWW ‘08 Proceedings of the 17th international conference on World Wide Web, pp. 1185–1186 (2008)Google Scholar
  8. 8.
    Gravano, L., Hatzivassiloglou, V., Lichtenstein, R.: Categorizing web queries according to geographical locality. In: Proceedings of the twelfth international conference on Information and knowledge management, pp. 325–333 (2003)Google Scholar
  9. 9.
    Coppola, P., Della Mea, V., Di Gaspero, L., Menegon, D., Mischis, D., Mizzaro, S., Scagnetto, I., Vassena, L.: CAB: the context-aware browser. IEEE Intell. Syst. 25(1), 38–47 (2010)CrossRefGoogle Scholar
  10. 10.
    Castelli, G., Mamei, M., Rosi, A.: The Whereabouts Diary, pp 175–192. Springer, Berlin (2007)Google Scholar
  11. 11.
    Gross, T., Klemke, R.: Context modelling for information retrieval: requirements and approaches. J. WWW/Internet 1, 29–42 (2003)Google Scholar
  12. 12.
    Jarke, M., Klemke, R., Nicki, A.: An Environment for Multi-Dimensional User-Adaptive Knowledge Management. IEEE Computer Society Press (2001)Google Scholar
  13. 13.
    Aréchiga, D., Vegas, J., Redondo, P.F.: Mymose: ontology supported personalized search for mobile devices. In: Proceedings of ONTOSE (2009)Google Scholar
  14. 14.
    Kessler, C.: What is the difference? A cognitive dissimilarity measure for information retrieval result sets. Knowl. Inf. Syst. 30(2), 319–340 (2012)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Stefanidis, K., Pitoura, E., Vassiliadis, P.: Adding context to preferences. In: Proceedings of the 23rd International Conference on Data Engineering (ICDE), p. 23 (2007)Google Scholar
  16. 16.
    Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. CHI 2000 Workshop on the What, Who, Where, When, Why and How of Context-Awareness (2000)Google Scholar
  17. 17.
    Diaz, F., Jones, R.: Using temporal profiles of queries for precision prediction. SIGIR’04 ACM J. 4 (2004)Google Scholar
  18. 18.
    Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275–281 (1998). Knowledge Information Systems J, 1–34 (2010)Google Scholar
  19. 19.
    Lavrenko, V., Croft, W.B.: Relevance-based language models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 120–127 (2001)Google Scholar
  20. 20.
    Jelinek, F., Mercer, R.: Interpolated estimation of Markov source parameters from sparse data. In: Proceedings of the Workshop on Pattern Recognition in Practice, Amsterdam (1980) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.LARODECISG University of TunisLe BardoTunisia
  2. 2.LARODECIHEC University of CarthageCarthageTunisia

Personalised recommendations