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Title

Improvement of Recommender Systems based on Reviews using Neural Attention Mechanism and LSTM

Year: 1399
COI: ECECON01_021
Language: EnglishView: 288
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Authors

Narges Farokhshad - Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Marjan Naderan - Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Mahmood Farokhian - Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract:

Recommender systems on online sales sites usually collect users' opinions about the products in two ways namely rating and reviews. In the reviews content, there is a lot of information that is less commonly used and they also differ in importance. This paper presents a review-basedrecommender system using a deep learning approach and the attention mechanism. This model consists of two parallelnetworks, one is trained to model the users and the other one to model the items. Each of these networks comprises the fourphases of: preprocessing, word embedding, feature extraction, and the attention mechanism. Then, in the last layer, the twonetworks are merged and with matrix factorization method, the final estimated rating is obtained. Simulation results of theproposed model are compared with two other models, namely DeepCoNN and NaRRe, and show that the proposed modelperforms better in terms of RMSE and MAE evaluation metrics.

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Paper COI Code

This Paper COI Code is ECECON01_021. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/1152598/

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If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
Farokhshad, Narges and Naderan, Marjan and Farokhian, Mahmood,1399,Improvement of Recommender Systems based on Reviews using Neural Attention Mechanism and LSTM,National Conference on Intelligent Systems and Fast Computing,Shahreza,https://civilica.com/doc/1152598

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Type of center: دانشگاه دولتی
Paper count: 16,323
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