Abstract
This paper presents an initial investigation in the relative effectiveness of different popular pseudo relevance feedback (PRF) methods. The retrieval performance of relevance model, and two KL-divergence-based divergence from randomness (DFR) feedback methods generalized from Rocchio’s algorithm, are compared by extensive experiments on standard TREC test collections. Results show that a KL-divergence based DFR method (denoted as KL1), combined with the classical Rocchio’s algorithm, has the best retrieval effectiveness out of the three methods studied in this paper.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Amati, G.: Probabilistic models for information retrieval based on divergence from randomness. PhD thesis, DCS, Univ. of Glasgow (2003)
Carpineto, C., de Mori, R., Romano, G., Bigi, B.: An information-theoretic approach to automatic query expansion. ACM Trans. Inf. Syst. 19(1), 1–27 (2001)
Lavrenko, V., Croft, W.B.: Relevance-Based Language Models. In: SIGIR, pp. 120–127 (2001)
Lv, Y., Zhai, C.: A comparative study of methods for estimating query language models with pseudo feedback. In: CIKM, pp. 1895–1898 (2009)
Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Gatford, M., Payne, A.: Okapi at TREC-4. In: TREC (1995)
Rocchio, J.: Relevance feedback in information retrieval. In: The SMART Retrieval System. Prentice-Hall, Englewood Cliffs (1971)
Ye, Z., He, B., Huang, X., Lin, H.: Revisiting Rocchio’s Relevance Feedback Algorithm for Probabilistic Models. In: Cheng, P.-J., Kan, M.-Y., Lam, W., Nakov, P. (eds.) AIRS 2010. LNCS, vol. 6458, pp. 151–161. Springer, Heidelberg (2010)
Zhai, C., Lafferty, J.D.: Model-based feedback in the language modeling approach to information retrieval. In: CIKM, pp. 403–410 (2001)
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
Hui, K., He, B., Luo, T., Wang, B. (2011). A Comparative Study of Pseudo Relevance Feedback for Ad-hoc Retrieval. In: Amati, G., Crestani, F. (eds) Advances in Information Retrieval Theory. ICTIR 2011. Lecture Notes in Computer Science, vol 6931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23318-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-23318-0_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23317-3
Online ISBN: 978-3-642-23318-0
eBook Packages: Computer ScienceComputer Science (R0)