Supervised Deep Polylingual Topic Modeling for Scholarly Information Recommendations

Pannawit Samatthiyadikun, Atsuhiro Takasu

Abstract

Polylingual text processing is important for content-based and hybrid recommender systems. It helps recommender systems extract content information from broader sources. It also enables systems to recommend items in a user’s native language. We propose a cross-lingual keyword recommendation method based on a polylingual topic model. The model is further extended with a popular deep learning architecture, the CNN–RNN model. With this model, keywords can be recommended from text written in different languages; model parameters are very meaningful, and we can interpret them. We evaluate the proposed method using crosslingual bibliographic databases that contain both English and Japanese abstracts and keywords.

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Paper Citation


in Harvard Style

Samatthiyadikun P. and Takasu A. (2018). Supervised Deep Polylingual Topic Modeling for Scholarly Information Recommendations.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 196-201. DOI: 10.5220/0006654901960201


in Bibtex Style

@conference{icpram18,
author={Pannawit Samatthiyadikun and Atsuhiro Takasu},
title={Supervised Deep Polylingual Topic Modeling for Scholarly Information Recommendations},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={196-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006654901960201},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Supervised Deep Polylingual Topic Modeling for Scholarly Information Recommendations
SN - 978-989-758-276-9
AU - Samatthiyadikun P.
AU - Takasu A.
PY - 2018
SP - 196
EP - 201
DO - 10.5220/0006654901960201