Abstract
Access to relevant information adapted to the needs and the context of the user is a real challenge. The user context can be assimilated to all factors that can describe his intentions and perceptions of his surroundings. It is difficult to find a contextual information retrieval system that takes into account all contextual factors. In this paper, both types of context user context and query context are integrated in an Information Retrieval (IR) model based on language modeling. Here, the query context include the integration of linguistic and semantic knowledge about the user query in order to explore the most exact understanding of user’s information needs. In addition, we consider one of the important factors of the user context, the user’s domain of interest or the interesting topic. A thematic algorithm is proposed to describe the user context. We assume that each topic can be characterized by a set of documents from the experimented corpus. The documents of each topic are used to build a statistical language model, which is then integrated to expand the original query model and to re-rank the retrieved documents. Our experiments on the 20_Newsgroup corpus show that the proposed contextual approach improves significantly the retrieval effectiveness compared to the basic approach, which does not consider contextual factors.
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References
Asfari, O., Doan, B.-L., Bourda, Y., Sansonnet, J.-P.: A Context-Based Model for Web Query Reformulation. In: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, KDIR 2010, Spain, Valencia (2010)
Asfari, O., Doan, B.-L., Bourda, Y., Sansonnet, J.-P.: Personalized Access to Contextual Information by using an Assistant for Query Reformulation. IARIA Journal, IntSys 2011 4(34) (2011)
Asfari, O.: Personnalisation et Adaptation de L’accès à L’information Contextuelle en utilisant un Assistant Intelligent. PhD thesis, Université Paris Sud - Paris XI, tel-00650115 - version 1 (September 19, 2011)
Allan, J.: Challenges in information retrieval and language modeling. In: Workshop Held at the Center for Intelligent Information Retrieval, University of Massachusetts, Amherst, SIGIR Forum vol. 37(1), pp. 31–47 (2003)
Bauer, T., Leake, D.: Real time user context modeling for information retrieval agents. In: CIKM 2001: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 568–570. ACM, Atlante (2001)
Billsus, D., Hilbert, D., Maynes-Aminzade, D.: Improving proactive information systems. In: IUI 2005: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 159–166. ACM, San Diego (2005)
Bai, J., Nie, J., Bouchard, H., Cao, H.: Using Query Contexts in Information Retrieval. In: SIGIR 2007, Amsterdam, Netherlands, July 23-27 (2007)
Bouchard, H., Nie, J.: Modèles de langue appliqués à la recherche d’information contextuelle. In: Proceedings of CORIA 2006 Conf. en Recherche d’Information et Applications, Lyon, pp. 213–224 (2006)
Conesa, J., Storey, V.C., Sugumaran, V.: Using Semantic Knowledge to Improve Web Query Processing. In: Kop, C., Fliedl, G., Mayr, H.C., Métais, E. (eds.) NLDB 2006. LNCS, vol. 3999, pp. 106–117. Springer, Heidelberg (2006)
Daoud, M., Tamine, L., Duy, D., Boughanem, M.: Contextual Query Classification For Personalizing Informational Search. In: Web Information Systems Engineering, Kerkennah Island, Sfax, Tunisia. ACM (Juin 2009)
Dey, A.K., Abowd, G.D.: Toward a better understanding of context and context-awareness. In: Workshop on the What, Who, Where, When, and How of Context-Awareness (1999)
Dumais, S., Cutrell, E., Cadiz, J.J., Jancke, G., Sarin, R., Robbins, D.C.: (Stuff I’ve Seen): A system for personal information retrieval and re-use. In: Proceedings of 26th ACM SIGIR 2003, Toronto, pp. 72–79 (July 2003)
Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: the concept revisited. In: WWW, Hong Kong (2001)
Henzinger, M., Chang, B.-W., Milch, B., Brin, S.: Query-free news search. In: The 12th International Conference on World Wide Web, Hungary (2003)
Hlaoua, L., Boughanem, M.: Towards Contextual and Structural Relevance Feedback in XML Retrieval. In: Beigbeder, M., Yee, W.G. (eds.) Workshop on Open Source Web Information Retrieval, Compiègne, pp. 35–38 (2005)
Ingwersen, P., Jäverlin, K.: Information retrieval in context. IRiX, ACM SIGIR Forum 39(2), 31–39 (2005)
Kelly, D., Teevan, J.: Implicit Feedback for Inferring User Preference: A Bibliography. SIGIR Forum 32(2), 18–28 (2003)
Kofod-Petersen, A., Cassens, J.: Using Activity Theory to Model Context Awareness. In: American Association for Artificial Intelligence, Berlin (2006)
Lafferty, J., Zhai, C.: Language models, query models, and risk minimization for information retrieval. In: SIGIR 2001, The 24th ACM International Conference on Research and Development in Information Retrieval, New York, pp. 111–119 (2001)
Lang, K.: NewsWeeder: learning to filter net news. In: The 12th International Conference on Machine Learning, San Mateo, USA, pp. 331–339 (1995)
Liu, X., Croft, W.B.: Statistical language modeling for information retrieval. In: Cronin, B. (ed.) Annual Review of Information Science and Technology, ch. 1, vol. 39 (2006)
Mylonas, P., Vallet, D., Castells, P., Fernandez, M., Avrithis, Y.: Personalized information retrieval based on context and ontological knowledge. Knowledge Engineering Review 23, 73–100 (2008)
Ratinov, L., Roth, D., Srikumar, V.: Conceptual search and text categorization. Technical Report UIUCDCS-R-2008-2932, UIUC, CS Dept. (2008)
Sauvagnat, K., Boughanem, M., Chrisment, C.: Answering content and structure-based queries on XML documents using relevance propagation. In: Information Systems, Numéro Spécial Special Issue, SPIRE 2004, vol. 31, pp. 621–635. Elsevier (2006)
Shen, X., Tan, B., Zhai, C.: Context-sensitive information retrieval using implicit feedback. In: SIGIR 2005: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–50. ACM, Brazil (2005)
Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive Web Search Based on User Profile Constructed without Any Effort from Users. In: WWW, New York, USA, pp. 17–22 (2004)
Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the CIKM 2001 Conference, pp. 403–410 (2001)
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Aknouche, R., Asfari, O., Bentayeb, F., Boussaid, O. (2012). Integrating Query Context and User Context in an Information Retrieval Model Based on Expanded Language Modeling. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds) Multidisciplinary Research and Practice for Information Systems. CD-ARES 2012. Lecture Notes in Computer Science, vol 7465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32498-7_19
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