Proceedings of 2nd Asian Conference on Machine Learning, PMLR 13:269-284, 2010.
Abstract
This paper focuses on exploring personalized multi-task learning approaches for collaborative filtering towards the goal of improving the prediction performance of rating prediction systems. These methods first specifically identify a set of users that are closely related to the user under consideration (i.e., active user), and then learn multiple rating prediction models simultaneously, one for the active user and one for each of the related users. Such learning for multiple models (tasks) in parallel is implemented by representing all learning instances (users and items) using a coupled user-item representation, and within errorinsensitive Support Vector Regression (e-SVR) framework applying multi-task kernel tricks. A comprehensive set of experiments shows that multi-task learning approaches lead to significant performance improvement over conventional alternatives.
@InProceedings{pmlr-v13-ning10a,
title = {Multi-task Learning for Recommender System},
author = {Xia Ning and George Karypis},
booktitle = {Proceedings of 2nd Asian Conference on Machine Learning},
pages = {269--284},
year = {2010},
editor = {Masashi Sugiyama and Qiang Yang},
volume = {13},
series = {Proceedings of Machine Learning Research},
address = {Tokyo, Japan},
month = {08--10 Nov},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v13/ning10a/ning10a.pdf},
url = {http://proceedings.mlr.press/v13/ning10a.html},
abstract = {This paper focuses on exploring personalized multi-task learning approaches for collaborative filtering towards the goal of improving the prediction performance of rating prediction systems. These methods first specifically identify a set of users that are closely related to the user under consideration (i.e., active user), and then learn multiple rating prediction models simultaneously, one for the active user and one for each of the related users. Such learning for multiple models (tasks) in parallel is implemented by representing all learning instances (users and items) using a coupled user-item representation, and within errorinsensitive Support Vector Regression (e-SVR) framework applying multi-task kernel tricks. A comprehensive set of experiments shows that multi-task learning approaches lead to significant performance improvement over conventional alternatives.}
}
%0 Conference Paper
%T Multi-task Learning for Recommender System
%A Xia Ning
%A George Karypis
%B Proceedings of 2nd Asian Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2010
%E Masashi Sugiyama
%E Qiang Yang
%F pmlr-v13-ning10a
%I PMLR
%J Proceedings of Machine Learning Research
%P 269--284
%U http://proceedings.mlr.press
%V 13
%W PMLR
%X This paper focuses on exploring personalized multi-task learning approaches for collaborative filtering towards the goal of improving the prediction performance of rating prediction systems. These methods first specifically identify a set of users that are closely related to the user under consideration (i.e., active user), and then learn multiple rating prediction models simultaneously, one for the active user and one for each of the related users. Such learning for multiple models (tasks) in parallel is implemented by representing all learning instances (users and items) using a coupled user-item representation, and within errorinsensitive Support Vector Regression (e-SVR) framework applying multi-task kernel tricks. A comprehensive set of experiments shows that multi-task learning approaches lead to significant performance improvement over conventional alternatives.
TY - CPAPER
TI - Multi-task Learning for Recommender System
AU - Xia Ning
AU - George Karypis
BT - Proceedings of 2nd Asian Conference on Machine Learning
PY - 2010/10/31
DA - 2010/10/31
ED - Masashi Sugiyama
ED - Qiang Yang
ID - pmlr-v13-ning10a
PB - PMLR
SP - 269
DP - PMLR
EP - 284
L1 - http://proceedings.mlr.press/v13/ning10a/ning10a.pdf
UR - http://proceedings.mlr.press/v13/ning10a.html
AB - This paper focuses on exploring personalized multi-task learning approaches for collaborative filtering towards the goal of improving the prediction performance of rating prediction systems. These methods first specifically identify a set of users that are closely related to the user under consideration (i.e., active user), and then learn multiple rating prediction models simultaneously, one for the active user and one for each of the related users. Such learning for multiple models (tasks) in parallel is implemented by representing all learning instances (users and items) using a coupled user-item representation, and within errorinsensitive Support Vector Regression (e-SVR) framework applying multi-task kernel tricks. A comprehensive set of experiments shows that multi-task learning approaches lead to significant performance improvement over conventional alternatives.
ER -
Ning, X. & Karypis, G.. (2010). Multi-task Learning for Recommender System. Proceedings of 2nd Asian Conference on Machine Learning, in PMLR 13:269-284
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