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Sentiment-aware deep recommender system with neural attention networks

With the advent of web technology, user-generated textual reviews are becoming increasingly accumulated on many e-commerce websites. These reviews contain not only the user comments on different aspects of the products but also the user sentiments associated with the aspects. Although these user sen...

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Main Authors: Da’u, Aminu, Salim, Naomie
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2019
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/89210/1/AminuDau2019_SentimentAwareDeepRecommenderSystem.pdf
http://eprints.utm.my/id/eprint/89210/
http://dx.doi.org/10.1109/ACCESS.2019.2907729
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spelling my.utm.892102021-02-22T06:00:58Z http://eprints.utm.my/id/eprint/89210/ Sentiment-aware deep recommender system with neural attention networks Da’u, Aminu Salim, Naomie QA75 Electronic computers. Computer science With the advent of web technology, user-generated textual reviews are becoming increasingly accumulated on many e-commerce websites. These reviews contain not only the user comments on different aspects of the products but also the user sentiments associated with the aspects. Although these user sentiments serve as vital side information for improving the performance of recommender systems, most existing approaches ignore to fully exploit them in modeling the fine-grained user-item interaction for improving recommender system performance. Thus, this paper proposes a sentiment-aware deep recommender system with neural attention network (SDRA), which can capture both the aspects of products and the underlying user sentiments associated with the aspects for improving the recommendation system performance. Particularly, a semi-supervised topic model is designed to extract the aspects of the product and the associated sentiment lexicons from the user textual reviews, which are then incorporated into a long short term memory (LSTM) encoder via an interactive neural attention mechanism for better learning of the user and item sentiment-aware representation. Furthermore, a co-attention mechanism is introduced to better model the fine-grained user-item interaction for improving predictive performance. The extensive experiments on different datasets showed that our proposed SDRA model can achieve better performance over the baseline approaches. Institute of Electrical and Electronics Engineers Inc. 2019-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/89210/1/AminuDau2019_SentimentAwareDeepRecommenderSystem.pdf Da’u, Aminu and Salim, Naomie (2019) Sentiment-aware deep recommender system with neural attention networks. IEEE Access, 7 . pp. 45472-45484. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2019.2907729 DOI:10.1109/ACCESS.2019.2907729
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Da’u, Aminu
Salim, Naomie
Sentiment-aware deep recommender system with neural attention networks
description With the advent of web technology, user-generated textual reviews are becoming increasingly accumulated on many e-commerce websites. These reviews contain not only the user comments on different aspects of the products but also the user sentiments associated with the aspects. Although these user sentiments serve as vital side information for improving the performance of recommender systems, most existing approaches ignore to fully exploit them in modeling the fine-grained user-item interaction for improving recommender system performance. Thus, this paper proposes a sentiment-aware deep recommender system with neural attention network (SDRA), which can capture both the aspects of products and the underlying user sentiments associated with the aspects for improving the recommendation system performance. Particularly, a semi-supervised topic model is designed to extract the aspects of the product and the associated sentiment lexicons from the user textual reviews, which are then incorporated into a long short term memory (LSTM) encoder via an interactive neural attention mechanism for better learning of the user and item sentiment-aware representation. Furthermore, a co-attention mechanism is introduced to better model the fine-grained user-item interaction for improving predictive performance. The extensive experiments on different datasets showed that our proposed SDRA model can achieve better performance over the baseline approaches.
format Article
author Da’u, Aminu
Salim, Naomie
author_facet Da’u, Aminu
Salim, Naomie
author_sort Da’u, Aminu
title Sentiment-aware deep recommender system with neural attention networks
title_short Sentiment-aware deep recommender system with neural attention networks
title_full Sentiment-aware deep recommender system with neural attention networks
title_fullStr Sentiment-aware deep recommender system with neural attention networks
title_full_unstemmed Sentiment-aware deep recommender system with neural attention networks
title_sort sentiment-aware deep recommender system with neural attention networks
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2019
url http://eprints.utm.my/id/eprint/89210/1/AminuDau2019_SentimentAwareDeepRecommenderSystem.pdf
http://eprints.utm.my/id/eprint/89210/
http://dx.doi.org/10.1109/ACCESS.2019.2907729
_version_ 1692991759872688128
score 13.211869

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