Abstract
Modeling users’ information interests and needs is one of the most important task in the personalization of information retrieval domain. Search engines’ algorithms are constantly improved but they still return a lot of irrelevant documents. For this reason a user is not able to quickly find a document with necessary information. In this paper we propose a user profile that is updated based on novel relevance judgment method. Proposed algorithms are a part of personalization agent system. As performed experimental evaluations have shown, the distance between current user profile and user preferences is decreasing when our algorithm is applied.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bhogal, J., Macfarlane, A., Smith, P.: A review of ontology based query expansion. Information Processing and Management 43, 866–886 (2007)
Crestani, F., Lalmas, M., Rijsbergen, C.J., Campbell, I.: Is This Document Relevant? …Probably”: A Survey of Probabilistic Models in Information Retrieval. ACM Computing Surveys 30(4) (1998)
Clarkea, C.L.A., Cormackb, G., Tudhope, E.A.: Relevance ranking for one to three term queries. Information Processing & Management 36, 291–311 (2000)
Greisdorf, H.: Relevance thresholds: a multi-stage predictive model of how users evaluate information. Information Processing and Management 39, 403–423 (2003)
Jung, S., Herlocker, J.L., Webster, J.: Click data as implicit relevance feedback in web search. Information Processing and Management 43, 791–807 (2007)
Kiewra, M.: The hybrid method for content recommendation in a hypertext environment, Ph.D. dissertation, Wroclaw University of Technology (2007)
Lavrenko, V., Croft, W.: B.: Relevance-Based Language Models. In: SIGIR 2001. ACM, New Orleans (2001)
Mianowska, B., Nguyen, N.T.: A Method for User Profile Adaptation in Document Retrieval. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS, vol. 6592, pp. 181–192. Springer, Heidelberg (2011)
Mianowska, B., Nguyen, N.T.: A Framework of an Agent-Based Personal Assistant for Internet Users. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds.) KES-AMSTA 2010. LNCS, vol. 6070, pp. 163–172. Springer, Heidelberg (2010)
Pan, J., Zhang, B., Wang, S., Wu, G., Wei, D.: Ontology Based User Profiling in Personalized Information Service Agent. In: CIT 2007, pp. 1089–1093. IEEE, Los Alamitos (2007)
Rijsbergen, C.J.: Information Retrieval, University of Glasgow (1979)
Robertson, S.E., Maron, M.E., Cooper, W.S.: Probability of Relevance: A Unification of Two Competing Models for Document Retrieval. Information Technology: Research and Development 1, 1–21 (1982)
Story, R.E.: An Explanation of the Effectiveness of Latent Semantic Indexing by Means of a Bayesian Regression Model. Information Processing and Management 32(3), 329–344 (1996)
Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive Web Search Based on User Profile Constructed without Any Effort from Users. In: WWW 2004, New York, USA (2004)
Wallis, P., Thom, J.A.: Relevance judgments for assessing recall. Information Processing and Management 32(3), 273–286 (1996)
Main Library and Scientific Information Centre in Wroclaw University of Technology, http://www.bg.pwr.wroc.pl/
 WordNet Ontology, http://wordnet.princeton.edu/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mianowska, B., Nguyen, N.T. (2011). A Method of User Modeling and Relevance Simulation in Document Retrieval Systems. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-22000-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21999-3
Online ISBN: 978-3-642-22000-5
eBook Packages: Computer ScienceComputer Science (R0)