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

سال انتشار: 1399
محل انتشار: کنفرانس ملی سیستم های هوشمند و محاسبات سریع
کد COI اختصاصی: ECECON01_021
زبان مقاله: انگلیسی
تعداد مشاهده: 732

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نویسندگان

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

چکیده

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.

کلیدواژه ها

text processing, recommender systems, LSTM, attention mechanism

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