Abstract
The idea of Relevance Feedback is to take the results that are initially returned from a given query and to use information about whether or not those results are relevant to perform a new query. The most commonly used Relevance Feedback methods aim to rewrite the user query. In the Vector Space Model, Relevance Feedback is usually undertaken by re-weighting the query terms without any modification in the vector space basis. With respect to the initial vector space basis(index terms), relevant and irrelevant documents share some terms (at least the terms of the query which selected these documents). In this paper we propose a new Relevance Feedback method based on vector space basis change without any modification on the query term weights. The aim of our method is to build a basis which optimally separates relevant and irrelevant documents. That is, this vector space basis gives a better representation of the documents such that the relevant documents are gathered and the irrelevant documents are kept away from the relevant ones.
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
Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004)
De Campos, L.M., Fernández-Luna, J.M., Huete, J.F.: Relevance feedback in the bayesian network retrieval model: An approach based on term instantiation. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, p. 13. Springer, Heidelberg (2001)
Cao, G., Nie, J.-Y., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 243–250. ACM, New York (2008)
Croft, W.B., Harper, D.: Using Probabilistic Models of Information without Relevance Information. Journal of Documentation 35(4), 285–295 (1979)
Croft, W.B., Lavrendo, S.C.T.V.: Relevance Feedback and Personalization: A Language Modelling Perspective. In: Proceedings of the Joint DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries, CIKM 2006, pp. 49–54 (2001)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the ASIS 41(6), 391–407 (1990)
Ide, E.: New Experiments in Relevance Feedback. In: The SMART Retrieval System-Experiments in Automatic Document Processing, pp. 337–354 (1971)
Lv, Y., Zhai, C.: Positional relevance model for pseudo-relevance feedback. In: SIGIR 2010, pp. 579–586. ACM, New York (2010)
Melucci, M.: Context Modeling and Discovery using Vector Space Bases. In: Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), Bremen, Germany, pp. 808–815. ACM Press (2005)
Melucci, M.: A basis for information retrieval in context. ACM Trans. Inf. Syst. 26(3), 1–41 (2008)
Mbarek, R., Tmar, M.: Relevance Feedback Method Based on Vector Space Basis Change. In: Calderón-Benavides, L., González-Caro, C., Chávez, E., Ziviani, N. (eds.) SPIRE 2012. LNCS, vol. 7608, pp. 342–347. Springer, Heidelberg (2012)
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
van Rijsbergen, C.J.: The Geometry of Information Retrieval. Cambridge University Press, UK (2004)
Robertson, S., Sparck Jones, J.K.: Relevance Weighting of Search Terms. Journal of the ASIS 27(3), 129–146 (1976)
Rocchio, J.: Relevance Feedback in Information Retrieval. The SMART retrieval system-experiments in automatic document processing, pp. 313–323. Prentice-Hall Inc. (1971)
Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. The Knowledge Engineering Review 18(2), 95–145 (2003)
Ruthven, I., Lalmas, M., Rijsbergen, K.: Ranking Expansion Terms with Partial and Ostensive Evidence. In: Fourth International Conference on Conceptions of Library and Information Science: Emerging Frameworks and Methods, Seattle WA, USA, pp. 199–219 (2002)
Sakai, T., Manabe, T., Koyama, M.: Flexible pseudo-relevance feedback via selective sampling. ACM Transactions on Asian Language Information Processing (TALIP) 4(2), 111–135 (2005)
Walker, S., Hancock-Beaulieu, M., Gull, A., Lau, M.: Okapi at TREC. In: TREC, pp. 21–30 (1992)
Tao, T., Zhai, C.: Regularized estimation of mixture models for robust pseudo-relevance feedback. In: SIGIR 2006, pp. 162–169. ACM Press, New York (2006)
Xu, Y., Jones, G.J., Wang, B.: Query dependent pseudo- relevance feedback based on wikipedia. In: SIGIR 2009, pp. 59–66. ACM, New York (2009)
Zhou, D., Lawless, S., Wade, V.: Improving search via personalized query expansion using social media. Information Retrieval 15, 218–242 (2012)
Zhou, D., Truran, M., Liu, J., Zhang, S.: Collaborative pseudo-relevance feedback. Expert Systems with Applications 40, 6805–6812 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mbarek, R., Tmar, M., Hattab, H. (2014). A New Relevance Feedback Algorithm Based on Vector Space Basis Change. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-54903-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54902-1
Online ISBN: 978-3-642-54903-8
eBook Packages: Computer ScienceComputer Science (R0)