A modern information retrieval system must have the capability
to find, organize and present very different manifestations of
information - such as text, pictures, videos or database records -
any of which may be of relevance to the user. However, the concept
of relevance, while seemingly intuitive, is actually hard to
define, and it's even harder to model in a formal way.
Lavrenko does not attempt to bring forth a new definition of
relevance, nor provide arguments as to why any particular
definition might be theoretically superior or more complete.
Instead, he takes a widely accepted, albeit somewhat conservative
definition, makes several assumptions, and from them develops a new
probabilistic model that explicitly captures that notion of
relevance. With this book, he makes two major contributions to the
field of information retrieval: first, a new way to look at topical
relevance, complementing the two dominant models, i.e., the
classical probabilistic model and the language modeling approach,
and which explicitly combines documents, queries, and relevance in
a single formalism; second, a new method for modeling exchangeable
sequences of discrete random variables which does not make any
structural assumptions about the data and which can also handle
rare events.
Thus his book is of major interest to researchers and graduate
students in information retrieval who specialize in relevance
modeling, ranking algorithms, and language modeling.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!