Novel Topic Models for Content Based Recommender Systems

Kamal Maanicshah, Manar Amayri, Nizar Bouguila

2023

Abstract

Content based recommender systems play a vital role in applications related to user suggestions. In this paper, we introduce novel topic models which help tackle the recommendation task. Being one of the prominent approaches in the field of natural language processing, topic models like latent Dirichlet allocation (LDA) try to identify patterns of topics across multiple documents. Due to the proven efficiency of generalized Dirichlet allocation and Beta-Liouville allocation in recent times, we use these models for better performance. In addition, since it is a known fact that co-occurences of words are commonplace in text documents, the models have been designed with this reality in mind. Our models follow a mixture based design to achieve better topic quality. We use variational inference for estimating the parameters. Our models are validated with two different datasets for recommendation tasks.

Paper Citation

in Harvard Style

Maanicshah K., Amayri M. and Bouguila N. (2023). Novel Topic Models for Content Based Recommender Systems. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 138-145. DOI: 10.5220/0011826700003467

in Bibtex Style

@conference{iceis23,
author={Kamal Maanicshah and Manar Amayri and Nizar Bouguila},
title={Novel Topic Models for Content Based Recommender Systems},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={138-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011826700003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Novel Topic Models for Content Based Recommender Systems
SN - 978-989-758-648-4
AU - Maanicshah K.
AU - Amayri M.
AU - Bouguila N.
PY - 2023
SP - 138
EP - 145
DO - 10.5220/0011826700003467
PB - SciTePress