Click & Collect
from 2 Hours*
Last Christmas
delivery dates
Free UK Standard Delivery On all orders over £25 Order in time for Christmas 19th December 2nd Class |
21st December by 3pm 1st Class
Free Click & Collect to shops From 2 hours after you order*
Information Retrieval Models: Foundations and Relationships - Synthesis Lectures on Information Concepts, Retrieval, and Services (Paperback)
  • Information Retrieval Models: Foundations and Relationships - Synthesis Lectures on Information Concepts, Retrieval, and Services (Paperback)
zoom

Information Retrieval Models: Foundations and Relationships - Synthesis Lectures on Information Concepts, Retrieval, and Services (Paperback)

(author)
£43.95
Paperback 163 Pages / Published: 30/07/2013
  • In stock
  • Free UK delivery

UK delivery within 1 week

  • This item has been added to your basket
Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR).

Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: ""It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works.""

This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models.

A particular focus of this book is on the ""relationships between models."" This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters.

Publisher: Morgan & Claypool Publishers
ISBN: 9781627050784
Number of pages: 163
Weight: 330 g
Dimensions: 235 x 191 x 9 mm

You may also be interested in...

Step by Step Databases
Added to basket
MySQL: The Complete Reference
Added to basket
Data Center Storage
Added to basket
Elasticsearch in Action
Added to basket
Data Literacy
Added to basket
£34.99
Paperback
Big Data For Dummies
Added to basket
£25.99
Paperback
Graphing Data with R
Added to basket
Data Mining For Dummies
Added to basket
£26.99
Paperback
Star Schema The Complete Reference
Added to basket
SQL Antipatterns
Added to basket
£27.99
Paperback
Statistics For Big Data For Dummies
Added to basket
Data Analysis Using SQL and Excel, 2e
Added to basket

Please sign in to write a review

Your review has been submitted successfully.