Authors:
Lamia Berkani
1
;
Lina Ighilaza
2
and
Fella Dib
2
Affiliations:
1
Department of Artificial Intelligence & Data Sciences, Faculty of Informatics, USTHB University, Algiers, Algeria
;
2
Department of Computer Science, Faculty of Informatics, USTHB University, Algiers, Algeria
Keyword(s):
Recommender System, Deep Learning, Hybrid Sentiment Analysis, Word Embedding, Confidence Matrix.
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 ap
proach significantly outperforms state-of-the-art baselines and related work.
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