Classical IR Ranking based on document's content
A Case Study in Web Search Using TREC Algorithms
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.
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
Evaluation of many combinations of term frequency statistics, document frequency statistics and document length normalization. [PDF]
Information Retrieval Tutorial
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
Formal introduction to latent semantic indexing. [PS format]
Probabilistic Models in Information Retrieval
Introduction to probabilistic models.
A Chapter in a book which introduces probabilistic retrieval.
"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.
Last update:January 2, 2007 at 19:57:16 UTC