Anyone who's used a computer to find information on
the Web knows that the experience can be frustrating.
Search engines are incorporating new techniques (such
as examining document link structures) to increase
effectiveness. However, searchers all too often face
one of two outcomes: reviewing many more Web pages than
they'd prefer or failing to find as much useful
information as they really want. We introduce a new
retrieval technique that exploits users' persistent
information needs. These users might include business
analysts specialising in genetic technologies,
stockbrokers keeping abreast of wireless
communications, and legislators needing to understand
computer privacy and security developments. To help
such searchers, we evolve effective search programs by
using feedback based on users' judgments about the
relevance of the documents they've retrieved. This
approach uses genetic programming to automatically
evolve new retrieval algorithms based on a user's
evaluation of previously viewed documents
IR, cosine nearness measure, keyword weighting.
Log. Pop=200. TREC 80000 documents. Large number (500)
papers returned to user.
GP way better in comparison with SMART (Singhal, 1996)
and ANN.
%0 Journal Article
%1 Gordon:2006:IS
%A Gordon, Michael
%A Fan, Weiguo (Patrick)
%A Pathak, Praveen
%D 2006
%J IEEE Intelligent Systems
%K Internet, Web adaptive algorithms, document engines, feedback, genetic information judgement needs needs, pages, persistent programming, relevance retrieval search search, technique, user
%N 5
%P 72--77
%R 10.1109/MIS.2006.86
%T Adaptive Web Search: Evolving a Program That Finds
Information
%V 21
%X Anyone who's used a computer to find information on
the Web knows that the experience can be frustrating.
Search engines are incorporating new techniques (such
as examining document link structures) to increase
effectiveness. However, searchers all too often face
one of two outcomes: reviewing many more Web pages than
they'd prefer or failing to find as much useful
information as they really want. We introduce a new
retrieval technique that exploits users' persistent
information needs. These users might include business
analysts specialising in genetic technologies,
stockbrokers keeping abreast of wireless
communications, and legislators needing to understand
computer privacy and security developments. To help
such searchers, we evolve effective search programs by
using feedback based on users' judgments about the
relevance of the documents they've retrieved. This
approach uses genetic programming to automatically
evolve new retrieval algorithms based on a user's
evaluation of previously viewed documents
@article{Gordon:2006:IS,
abstract = {Anyone who's used a computer to find information on
the Web knows that the experience can be frustrating.
Search engines are incorporating new techniques (such
as examining document link structures) to increase
effectiveness. However, searchers all too often face
one of two outcomes: reviewing many more Web pages than
they'd prefer or failing to find as much useful
information as they really want. We introduce a new
retrieval technique that exploits users' persistent
information needs. These users might include business
analysts specialising in genetic technologies,
stockbrokers keeping abreast of wireless
communications, and legislators needing to understand
computer privacy and security developments. To help
such searchers, we evolve effective search programs by
using feedback based on users' judgments about the
relevance of the documents they've retrieved. This
approach uses genetic programming to automatically
evolve new retrieval algorithms based on a user's
evaluation of previously viewed documents},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Gordon, Michael and Fan, Weiguo (Patrick) and Pathak, Praveen},
biburl = {https://www.bibsonomy.org/bibtex/29c67a8953265aa648434457c5ee77128/brazovayeye},
doi = {10.1109/MIS.2006.86},
interhash = {69675e55c93da78b63cd4b199e2ae5a0},
intrahash = {9c67a8953265aa648434457c5ee77128},
issn = {1541-1672},
journal = {IEEE Intelligent Systems},
keywords = {Internet, Web adaptive algorithms, document engines, feedback, genetic information judgement needs needs, pages, persistent programming, relevance retrieval search search, technique, user},
month = {September-October},
notes = {IR, cosine nearness measure, keyword weighting.
Log. Pop=200. TREC 80000 documents. Large number (500)
papers returned to user.
GP way better in comparison with SMART (Singhal, 1996)
and ANN.},
number = 5,
pages = {72--77},
size = {6 pages},
timestamp = {2008-06-19T17:40:30.000+0200},
title = {Adaptive Web Search: Evolving a Program That Finds
Information},
volume = 21,
year = 2006
}