Use of Text Mining Techniques for Recommender Systems

Yanelys Betancourt, Sergio Ilarri

Abstract

Recommender systems help users to reduce the information overload they may suffer in the current era of Big Data, by offering them recommendations of relevant items according to their tastes/preferences and/or context (location, weather, time of the day, etc.). We argue that text mining techniques can be exploited for the development of recommender systems. Thus, they can be applied to detect user preferences (user profiling) and also to extract context data. For this purpose, text mining can be applied on user reviews, text descriptions associated to the items, and other texts written by the user (e.g., posts in social networks). In this paper, we provide an overview of works exploiting text mining techniques in the field of recommender systems, characterizing them according to their purpose and the type of textual data analyzed.

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Paper Citation


in Harvard Style

Betancourt Y. and Ilarri S. (2020). Use of Text Mining Techniques for Recommender Systems.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 780-787. DOI: 10.5220/0009576507800787


in Bibtex Style

@conference{iceis20,
author={Yanelys Betancourt and Sergio Ilarri},
title={Use of Text Mining Techniques for Recommender Systems},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={780-787},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009576507800787},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Use of Text Mining Techniques for Recommender Systems
SN - 978-989-758-423-7
AU - Betancourt Y.
AU - Ilarri S.
PY - 2020
SP - 780
EP - 787
DO - 10.5220/0009576507800787