MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||5|
|Section||Digital Signal and Image Processing|
|Published online||19 June 2018|
Recommender systems based on opinion mining and deep neural networks
College of Software, Jilin University, Changchun, China
2 College of Computer Science and Technology, Jilin University, Changchun, China
* Corresponding author:firstname.lastname@example.org
To address rating sparsity problem, various review-based recommender systems have been developed in recent years. Most of them extract topics, opinions, and emotional polarity from the reviews by using the techniques of text analysis and opinion mining. According to existing researches, review-based recommendation methods utilize review elements in rating prediction model, but underuse the actual ratings provided by users. In this paper, we adopt one lexicon-based opinion mining method to extract opinions hidden in reviews, and also, we combine opinions with actual ratings. In addition, we embed deep neural networks model which breaks through the limitation of traditional collaborative filtering. The experimental results based on two public datasets indicate that this personalized model provides an effective recommendation performance.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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