FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.
%0 Conference Paper
%1 dominguezgarcia2012freset
%A Dom\'ınguez Garc\'ıa, Renato
%A Bender, Matthias
%A Anjorin, Mojisola
%A Rensing, Christoph
%A Steinmetz, Ralf
%B Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
%C New York, NY, USA
%D 2012
%I ACM
%K evaluation folksonomy framework freset
%P 25--28
%R 10.1145/2365934.2365939
%T FReSET: an evaluation framework for folksonomy-based recommender systems
%U http://doi.acm.org/10.1145/2365934.2365939
%X FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.
%@ 978-1-4503-1638-5
@inproceedings{dominguezgarcia2012freset,
abstract = {FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.},
acmid = {2365939},
added-at = {2013-01-22T10:16:39.000+0100},
address = {New York, NY, USA},
author = {Dom\'{\i}nguez Garc\'{\i}a, Renato and Bender, Matthias and Anjorin, Mojisola and Rensing, Christoph and Steinmetz, Ralf},
biburl = {https://www.bibsonomy.org/bibtex/2c78b033eb1b463ff00c4fc67ed8bf679/sdo},
booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
description = {FReSET},
doi = {10.1145/2365934.2365939},
interhash = {489207308b5d7f064163652763794ce6},
intrahash = {c78b033eb1b463ff00c4fc67ed8bf679},
isbn = {978-1-4503-1638-5},
keywords = {evaluation folksonomy framework freset},
location = {Dublin, Ireland},
numpages = {4},
pages = {25--28},
publisher = {ACM},
series = {RSWeb '12},
timestamp = {2013-01-22T10:16:40.000+0100},
title = {FReSET: an evaluation framework for folksonomy-based recommender systems},
url = {http://doi.acm.org/10.1145/2365934.2365939},
year = 2012
}