Recommendation system exploiting aspect-based opinion mining with deep learning method

With the developments of e-commerce websites, user textual review has become an important source of information for improving the performance of recommendation systems, as they contain fine-grained users' opinions that generally reflect their preference towards products. However, most of the classical recommender systems (RSs) often ignore such user opinions and therefore fail to precisely capture users' specific sentiments on products. Although a few of the approaches have attempted to utilize fine-grained users' opinions for enhancing the accuracy of recommendation systems to some extent, most of these methods basically rely on handcrafted and rule-based approaches that are generally known to be time-consuming and labour-intensive. As such, their application is limited in practice. Thus, to overcome the above problems, this paper proposes a recommendation system that utilizes aspect-based opinion mining (ABOM) based on the deep learning technique to improve the accuracy of the recommendation process. The proposed model consists of two parts: ABOM and rating prediction. In the first part, we use a multichannel deep convolutional neural network (MCNN) to better extract aspects and generate aspect-specific ratings by computing users' sentiment polarities on various aspects. In the second part, we integrate the aspect-specific ratings into a tensor factorization (TF) machine for the overall rating prediction. Experimental results using various datasets show that our proposed model achieves significant improvements compared with the baseline methods. (C) 2019 Elsevier Inc. All rights reserved.