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A Case Study in Web Search Using TREC Algorithms http://www10.org/cdrom/papers/317/
This study evaluates the performance of a state-of-the-art keyword-based document ranking algorithm (coming out of TREC) on a popular web search task.
Document Ranking and the Vector-Space Model. http://www.cs.ust.hk/~dlee/Papers/ir/ieee-sw-rank.ps.gz
It describes key issues in document ranking techniques based on the vector­ space model. Several TF*IDF variants are discussed. The cosine measure, recall and precision are introduced. [PS format]
Exploring the Similarity Space http://goanna.cs.rmit.edu.au/~jz/fulltext/sigirforum98.pdf
Evaluation of many combinations of term frequency statistics, document frequency statistics and document length normalization.
Information Retrieval Tutorial http://isp.imm.dtu.dk/thor/projects/multimedia/textmining/
Description of boolean retrieval, vector space model, probabilistic retrieval, latent semantic indexing and other IR topics. An introduction to various classical ranking methods is also provided.
Latent Semantic Indexing: a Probabilistic Analysis http://www.cs.berkeley.edu/~christos/ir.ps
Formal introduction to latent semantic indexing. [PS format]
Probabilistic Models in Information Retrieval http://www.is.informatik.uni-duisburg.de/bib/docs/Fuhr_92.html
Introduction to probabilistic models.
Probabilistic Retrieval http://www.dcs.gla.ac.uk/Keith/Chapter.6/Ch.6.html
A Chapter in a book which introduces probabilistic retrieval.
Ranking Algorithms http://www.dcc.uchile.cl/~rbaeza/iradsbook/irbook.html
"Ranking Algorithms" is chapter 14 in the Frakes and Baeza-Yates book. It gives a good discussion of the tradeoffs and choices among different term-weighting strategies.

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