I like it... I like it not: Evaluating User Ratings Noise in Recommender Systems
X. Amatriain, J. Pujol, и N. Oliver. to-appear in Proceedings of the 1st and 17th International Conference on User Modeling, Adaptation and Personalization, Springer Berlin / Heidelberg, (2009)
Аннотация
Recent growing interest in predicting and influencing consumer behavior has generated
a parallel increase in research efforts on Recommender Systems. Many of the
state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order
to later predict unknown ratings. An underlying assumption in this approach is that
the user ratings can be treated as ground truth of the user’s taste. However, users are inconsistent
in giving their feedback, thus introducing an unknown amount of noise that
challenges the validity of this assumption.
In this paper, we tackle the problem of analyzing and characterizing the noise in
user feedback through ratings of movies. We present a user study aimed at quantifying
the noise in user ratings that is due to inconsistencies. We measure RMSE values that
range from 0.557 to 0.8156.We also analyze how factors such as item sorting and time
of rating affect this noise.
%0 Conference Paper
%1 paper:amatriain:2009
%A Amatriain, Xavier
%A Pujol, Josep M.
%A Oliver, Nuria
%B to-appear in Proceedings of the 1st and 17th International Conference on User Modeling, Adaptation and Personalization
%D 2009
%I Springer Berlin / Heidelberg
%K RS rating recommender user user-behaviour
%T I like it... I like it not: Evaluating User Ratings Noise in Recommender Systems
%U http://www.nuriaoliver.com/RecSys/LikeIt_umap09.pdf
%X Recent growing interest in predicting and influencing consumer behavior has generated
a parallel increase in research efforts on Recommender Systems. Many of the
state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order
to later predict unknown ratings. An underlying assumption in this approach is that
the user ratings can be treated as ground truth of the user’s taste. However, users are inconsistent
in giving their feedback, thus introducing an unknown amount of noise that
challenges the validity of this assumption.
In this paper, we tackle the problem of analyzing and characterizing the noise in
user feedback through ratings of movies. We present a user study aimed at quantifying
the noise in user ratings that is due to inconsistencies. We measure RMSE values that
range from 0.557 to 0.8156.We also analyze how factors such as item sorting and time
of rating affect this noise.
@inproceedings{paper:amatriain:2009,
abstract = {Recent growing interest in predicting and influencing consumer behavior has generated
a parallel increase in research efforts on Recommender Systems. Many of the
state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order
to later predict unknown ratings. An underlying assumption in this approach is that
the user ratings can be treated as ground truth of the user’s taste. However, users are inconsistent
in giving their feedback, thus introducing an unknown amount of noise that
challenges the validity of this assumption.
In this paper, we tackle the problem of analyzing and characterizing the noise in
user feedback through ratings of movies. We present a user study aimed at quantifying
the noise in user ratings that is due to inconsistencies. We measure RMSE values that
range from 0.557 to 0.8156.We also analyze how factors such as item sorting and time
of rating affect this noise.},
added-at = {2009-06-10T13:58:52.000+0200},
author = {Amatriain, Xavier and Pujol, Josep M. and Oliver, Nuria},
biburl = {https://www.bibsonomy.org/bibtex/254311211c6da8ebe5813fc785aafeadf/mschuber},
booktitle = {to-appear in Proceedings of the 1st and 17th International Conference on User Modeling, Adaptation and Personalization},
interhash = {f34c21b12d3abf33bc512fcccdbe9531},
intrahash = {54311211c6da8ebe5813fc785aafeadf},
keywords = {RS rating recommender user user-behaviour},
publisher = {Springer Berlin / Heidelberg},
timestamp = {2009-06-10T13:58:52.000+0200},
title = {I like it... I like it not: Evaluating User Ratings Noise in Recommender Systems},
url = {http://www.nuriaoliver.com/RecSys/LikeIt_umap09.pdf},
year = 2009
}