Abstract
The searching algorithms provide the filtering of the indexed items to create an ordered list of items with the most likely relevant items at the top. The search process uses the users query as a starting point in determining what items are most likely relevant to the user. This process revolves around a similarity measure formula that is used along with any heuristics added to improve the basic formula. There are many different similarity measures that are presented along with the rationale behind their use. Search results can be improved when there is feedback from a search on which hits are relevant and which are not—use of relevance feedback. Searches against multimedia items either follow textual search similarity approaches or use proprietary algorithms based upon the specific indexing for the multimedia modality.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsAuthor information
Authors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer US
About this chapter
Cite this chapter
Kowalski, G. (2011). Search. In: Information Retrieval Architecture and Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7716-8_5
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7716-8_5
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-7715-1
Online ISBN: 978-1-4419-7716-8
eBook Packages: Computer ScienceComputer Science (R0)