Real-life recommender systems often face the daunting task
of providing recommendations based only on the clicks of
a user session. Methods that rely on user profiles – such
as matrix factorization – perform very poorly in this setting,
thus item-to-item recommendations are used most of
the time. However the items typically have rich feature representations
such as pictures and text descriptions that can
be used to model the sessions. Here we investigate how these
features can be exploited in Recurrent Neural Network based
session models using deep learning. We show that obvious
approaches do not leverage these data sources. We thus introduce
a number of parallel RNN (p-RNN) architectures to
model sessions based on the clicks and the features (images
and text) of the clicked items. We also propose alternative
training strategies for p-RNNs that suit them better than
standard training. We show that p-RNN architectures with
proper training have significant performance improvements
over feature-less session models while all session-based models
outperform the item-to-item type baseline.
%0 Conference Paper
%1 hidasi2016parallel
%A Hidasi, Balázs
%A Quadrana, Massimo
%A Karatzoglou, Alexandros
%A Tikk, Domonkos
%B Proceedings of the 10th ACM Conference on Recommender Systems
%D 2016
%K recommendation rnn session
%P 241--248
%T Parallel recurrent neural network architectures for feature-rich session-based recommendations
%X Real-life recommender systems often face the daunting task
of providing recommendations based only on the clicks of
a user session. Methods that rely on user profiles – such
as matrix factorization – perform very poorly in this setting,
thus item-to-item recommendations are used most of
the time. However the items typically have rich feature representations
such as pictures and text descriptions that can
be used to model the sessions. Here we investigate how these
features can be exploited in Recurrent Neural Network based
session models using deep learning. We show that obvious
approaches do not leverage these data sources. We thus introduce
a number of parallel RNN (p-RNN) architectures to
model sessions based on the clicks and the features (images
and text) of the clicked items. We also propose alternative
training strategies for p-RNNs that suit them better than
standard training. We show that p-RNN architectures with
proper training have significant performance improvements
over feature-less session models while all session-based models
outperform the item-to-item type baseline.
@inproceedings{hidasi2016parallel,
abstract = {Real-life recommender systems often face the daunting task
of providing recommendations based only on the clicks of
a user session. Methods that rely on user profiles – such
as matrix factorization – perform very poorly in this setting,
thus item-to-item recommendations are used most of
the time. However the items typically have rich feature representations
such as pictures and text descriptions that can
be used to model the sessions. Here we investigate how these
features can be exploited in Recurrent Neural Network based
session models using deep learning. We show that obvious
approaches do not leverage these data sources. We thus introduce
a number of parallel RNN (p-RNN) architectures to
model sessions based on the clicks and the features (images
and text) of the clicked items. We also propose alternative
training strategies for p-RNNs that suit them better than
standard training. We show that p-RNN architectures with
proper training have significant performance improvements
over feature-less session models while all session-based models
outperform the item-to-item type baseline.},
added-at = {2016-09-30T11:37:38.000+0200},
author = {Hidasi, Bal{\'a}zs and Quadrana, Massimo and Karatzoglou, Alexandros and Tikk, Domonkos},
biburl = {https://www.bibsonomy.org/bibtex/243bc70eccf86a9c6f23e6b0e19eca682/dallmann},
booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems},
interhash = {8cfe83205c2eeb02776970f155acfd7d},
intrahash = {43bc70eccf86a9c6f23e6b0e19eca682},
keywords = {recommendation rnn session},
organization = {ACM},
pages = {241--248},
timestamp = {2016-09-30T12:05:23.000+0200},
title = {Parallel recurrent neural network architectures for feature-rich session-based recommendations},
year = 2016
}