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Authors: Pannawit Samatthiyadikun 1 and Atsuhiro Takasu 2

Affiliations: 1 The Graduate University for Advanced Studies (SOKENDAI), Japan ; 2 The Graduate University for Advanced Studies (SOKENDAI) and National Institute of Informatics (NII), Japan

ISBN: 978-989-758-276-9

Keyword(s): Deep Generative Topic Model, Recommender Systems, Multiple Information Sources.

Related Ontology Subjects/Areas/Topics: Applications ; Data Engineering ; Graphical and Graph-Based Models ; Information Retrieval ; Ontologies and the Semantic Web ; Pattern Recognition ; Software Engineering ; Theory and Methods ; Web Applications

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 several formats:
Samatthiyadikun, P.; 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

@conference{icpram18,
author={Pannawit Samatthiyadikun. and 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},
}

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 - Samatthiyadikun, P.
AU - Takasu, A.
PY - 2018
SP - 196
EP - 201
DO - 10.5220/0006654901960201

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