Definition
A model of information retrieval (IR) selects and ranks the relevant documents with respect to a user’s query. The texts of the documents and the queries are represented in the same way, so that document selection and ranking can be formalized by a matching function that returns a retrieval status value (RSV) for each document in the collection. Most of the IR systems represent document contents by a set of descriptors, called terms, belonging to a vocabulary V.
An IR model defines the query-document matching function according to four main approaches:
The estimation of the probability of user’s relevance rel for each document d and query q with respect to a set Rq of training documents
$$ \mathrm{Prob}0.24em \left( rel|\mathbf{d},\mathbf{q},{R}_{\mathbf{q}}\right) $$The computation of a similarity function between queries and documents in a vector space
$$...
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Recommended Reading
Robertson SE, Walker S. Some simple approximations to the 2-Poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval. Dublin: Springer; 1994. p. 232–41.
Salton G, McGill MJ. Introduction to modern information retrieval. New York: McGraw-Hill; 1983.
Ponte J, Croft BA. Language modeling approach in information retrieval. In: Croft B, Moffat A, Van Rijsbergen CJ, editors. Proceedings of the 21st ACM SIGIR conference on research and development in information retrieval. Melbourne: ACM; 1998. p. 275–81.
Amati G, Van Rijsbergen CJ. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans Inform Syst. 2002;20(4):357–89.
Clinchant S, Gaussier E. Information-based models for ad hoc IR. In: Proceedings of the 33rd ACM SIGIR conference on research and development in information retrieval. New York: ACM; 2010. p. 234–41.
Harter SP. A probabilistic approach to automatic keyword indexing. Part I: on the distribution of specialty words in a technical literature. J ASIS. 1975;26:197–216.
Berger A, Lafferty J. Information retrieval as statistical translation. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval; 1999. p. 222–9.
Croft WB, Lafferty J (Eds). Language modeling for information retrieval. Boston: Kluwer; 2003.
Turtle H, Bruce Croft W. Evaluation of an inference network-based retrieval model. ACM Trans Inform Syst. 1991;9(3):187–222.
Salton G, Fox EA, Wu H. Extended boolean information retrieval. Commun ACM. 1983;26(11):1022–36.
Van Rijsbergen CJA. New theorethical framework for information retrieval. In: Proceedings of the 9th annual international ACM SIGIR conference on research and development in information retrieval; 1986. p. 194–200.
Crestani F, Van Rijsbergen CJ. Probability kinematics in information retrieval. In: Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval; 1995. p. 291–9.
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Amati, G. (2017). Information Retrieval Models. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_916-2
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DOI: https://doi.org/10.1007/978-1-4899-7993-3_916-2
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