Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommendation. This note introduces PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Users are provided with a visual explanation of the CF process and opportunity to manipulate their neighborhood at varying levels of granularity to reflect aspects of their current requirements. In this manner we overcome the problem of redundant profile information in CF systems, in addition to providing an explanation interface. Our layout algorithm produces an exact, noiseless graph representation of the underlying correlations between users. PeerChooser's prediction component uses this graph directly to yield the same results as the benchmark. User's then improve on these predictions by tweaking the graph to their current requirements. We present a user-survey in which PeerChooser compares favorably against a benchmark CF algorithm.
%0 Conference Paper
%1 citeulike:2931394
%A O'Donovan, John
%A Smyth, Barry
%A Gretarsson, Brynjar
%A Bostandjiev, Svetlin
%A Höllerer, Tobias
%B CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems
%C New York, NY, USA
%D 2008
%I ACM
%K dlpaws en information-exploration information-visualization recommender
%P 1085--1088
%R 10.1145/1357054.1357222
%T PeerChooser: visual interactive recommendation
%U http://dx.doi.org/10.1145/1357054.1357222
%X Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommendation. This note introduces PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Users are provided with a visual explanation of the CF process and opportunity to manipulate their neighborhood at varying levels of granularity to reflect aspects of their current requirements. In this manner we overcome the problem of redundant profile information in CF systems, in addition to providing an explanation interface. Our layout algorithm produces an exact, noiseless graph representation of the underlying correlations between users. PeerChooser's prediction component uses this graph directly to yield the same results as the benchmark. User's then improve on these predictions by tweaking the graph to their current requirements. We present a user-survey in which PeerChooser compares favorably against a benchmark CF algorithm.
%@ 9781605580111
@inproceedings{citeulike:2931394,
abstract = {Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommendation. This note introduces PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Users are provided with a visual explanation of the CF process and opportunity to manipulate their neighborhood at varying levels of granularity to reflect aspects of their current requirements. In this manner we overcome the problem of redundant profile information in CF systems, in addition to providing an explanation interface. Our layout algorithm produces an exact, noiseless graph representation of the underlying correlations between users. PeerChooser's prediction component uses this graph directly to yield the same results as the benchmark. User's then improve on these predictions by tweaking the graph to their current requirements. We present a user-survey in which PeerChooser compares favorably against a benchmark CF algorithm.},
added-at = {2009-07-01T11:12:30.000+0200},
address = {New York, NY, USA},
author = {O'Donovan, John and Smyth, Barry and Gretarsson, Brynjar and Bostandjiev, Svetlin and H\"{o}llerer, Tobias},
biburl = {https://www.bibsonomy.org/bibtex/2f99aeb6c8ce6afe1d0272258ff555a64/brusilovsky},
booktitle = {CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems},
citeulike-article-id = {2931394},
doi = {10.1145/1357054.1357222},
interhash = {aa88de0e5bb9c42cb824eeea6c230f75},
intrahash = {f99aeb6c8ce6afe1d0272258ff555a64},
isbn = {9781605580111},
keywords = {dlpaws en information-exploration information-visualization recommender},
pages = {1085--1088},
posted-at = {2008-06-26 17:12:29},
priority = {5},
publisher = {ACM},
timestamp = {2009-07-01T11:12:36.000+0200},
title = {PeerChooser: visual interactive recommendation},
url = {http://dx.doi.org/10.1145/1357054.1357222},
year = 2008
}