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ABSTRACT
This article surveys probablistic approaches to modeling information retrieval. The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented. The various models proposed in the development of IR are described, classified, and compared using a common formalism. New approaches that constitute the basis of future research are described.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Makoto Nakashima , Keizo Sato , Yanhua Qu , Tetsuro Ito, Browsing-based conceptual information retrieval incorporating dictionary term relations, keyword association, and a user's interest, Journal of the American Society for Information Science and Technology, v.54 n.1, p.16-28, January 2003
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Marcos André Gonçalves , Edward A. Fox , Layne T. Watson , Neill A. Kipp, Streams, structures, spaces, scenarios, societies (5s): A formal model for digital libraries, ACM Transactions on Information Systems (TOIS), v.22 n.2, p.270-312, April 2004
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Zheng Chen , Fan Lin , Huan Liu , Yin Liu , Wei-Ying Ma , Liu Wenyin, User Intention Modeling in Web Applications Using Data Mining, World Wide Web, v.5 n.3, p.181-191, 2002
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REVIEW
"Karen Sparck-Jones : Reviewer"
This useful review provides a competent, clear, and
accessible account of retrieval models that take
probability as their grounding notion in defining the relevance relation
between queries and documents. These models ar
more...
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