Recently, Artificial Intelligence techniques have proved useful in
helping users to handle the large amount of information on the Internet.
The idea of personalized search engines, intelligent software agents,
and recommender systems has been widely accepted among users who require
assistance in searching, sorting, classifying, filtering and sharing
this vast quantity of information. In this paper, we present a
state-of-the-art taxonomy of intelligent recommender agents on the
Internet. We have analyzed 37 different systems and their references and
have sorted them into a list of 8 basic dimensions. These dimensions are
then used to establish a taxonomy under which the systems analyzed are
classified. Finally, we conclude this paper with a cross-dimensional
analysis with the aim of providing a starting point for researchers to
construct their own recommender system.
%0 Journal Article
%1 citeulike:783518
%A Montaner, Miquel
%A López, Beatriz
%A De La Rosa, Josep L.
%C Norwell, MA, USA
%D 2003
%I Kluwer Academic Publishers
%J Artif. Intell. Rev.
%K recommender, review
%N 4
%P 285--330
%R 10.1023/a:1022850703159
%T A Taxonomy of Recommender Agents on theInternet
%U http://dx.doi.org/10.1023/a:1022850703159
%V 19
%X Recently, Artificial Intelligence techniques have proved useful in
helping users to handle the large amount of information on the Internet.
The idea of personalized search engines, intelligent software agents,
and recommender systems has been widely accepted among users who require
assistance in searching, sorting, classifying, filtering and sharing
this vast quantity of information. In this paper, we present a
state-of-the-art taxonomy of intelligent recommender agents on the
Internet. We have analyzed 37 different systems and their references and
have sorted them into a list of 8 basic dimensions. These dimensions are
then used to establish a taxonomy under which the systems analyzed are
classified. Finally, we conclude this paper with a cross-dimensional
analysis with the aim of providing a starting point for researchers to
construct their own recommender system.
@article{citeulike:783518,
abstract = {{Recently, Artificial Intelligence techniques have proved useful in
helping users to handle the large amount of information on the Internet.
The idea of personalized search engines, intelligent software agents,
and recommender systems has been widely accepted among users who require
assistance in searching, sorting, classifying, filtering and sharing
this vast quantity of information. In this paper, we present a
state-of-the-art taxonomy of intelligent recommender agents on the
Internet. We have analyzed 37 different systems and their references and
have sorted them into a list of 8 basic dimensions. These dimensions are
then used to establish a taxonomy under which the systems analyzed are
classified. Finally, we conclude this paper with a cross-dimensional
analysis with the aim of providing a starting point for researchers to
construct their own recommender system.}},
added-at = {2017-11-15T17:02:25.000+0100},
address = {Norwell, MA, USA},
author = {Montaner, Miquel and L\'{o}pez, Beatriz and De La Rosa, Josep L.},
biburl = {https://www.bibsonomy.org/bibtex/2833d4e69aaedf5e39a2ed30541e04427/brusilovsky},
citeulike-article-id = {783518},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=640491},
citeulike-linkout-1 = {http://dx.doi.org/10.1023/a:1022850703159},
citeulike-linkout-2 = {http://www.springerlink.com/content/kk844421t5466k35},
day = 1,
doi = {10.1023/a:1022850703159},
interhash = {a3420bb4fdd76a9c15b1cb77eeb993af},
intrahash = {833d4e69aaedf5e39a2ed30541e04427},
issn = {0269-2821},
journal = {Artif. Intell. Rev.},
keywords = {recommender, review},
month = jun,
number = 4,
pages = {285--330},
posted-at = {2009-04-01 20:11:56},
priority = {2},
publisher = {Kluwer Academic Publishers},
timestamp = {2017-11-15T17:02:25.000+0100},
title = {{A Taxonomy of Recommender Agents on theInternet}},
url = {http://dx.doi.org/10.1023/a:1022850703159},
volume = 19,
year = 2003
}