Attentional Sentiment and Confidence Aware Neural Recommender Model

Lamia Berkani, Lina Ighilaza, Fella Dib

2023

Abstract

One of the major problems of recommendation systems is the rating data sparseness and information overload. To address these issues, some studies are leveraging review information to construct an accurate user/item latent factor. We propose in this article a neural hybrid recommender model based on attentional hybrid sentiment analysis, using BERT word embedding and deep learning models. An attention mechanism is used to capture the most relevant information. As reviews may contain misleading information (" fake good reviews / fake bad reviews "), a confidence matrix has been used to measure the relationship between rating outliers and misleading reviews. Then, the sentiment analysis module with fake reviews detection is used to update the user-item rating matrix. Finally, a hybrid recommendation is processed by combining the generalized matrix factorization (GMF) and the multilayer perceptron (MLP). The results of experiments on two datasets from the Amazon database show that our approach significantly outperforms state-of-the-art baselines and related work.

Paper Citation

in Harvard Style

Berkani L., Ighilaza L. and Dib F. (2023). Attentional Sentiment and Confidence Aware Neural Recommender Model. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 323-330. DOI: 10.5220/0012193000003598

in Bibtex Style

@conference{kdir23,
author={Lamia Berkani and Lina Ighilaza and Fella Dib},
title={Attentional Sentiment and Confidence Aware Neural Recommender Model},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012193000003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Attentional Sentiment and Confidence Aware Neural Recommender Model
SN - 978-989-758-671-2
AU - Berkani L.
AU - Ighilaza L.
AU - Dib F.
PY - 2023
SP - 323
EP - 330
DO - 10.5220/0012193000003598
PB - SciTePress