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Weighted aspect-based opinion mining using deep learning for recommender system

机译：使用深度学习推荐系统的基于方面的加权观点挖掘

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摘要

The main goal of Aspect-Based Opinion Mining is to extract product's aspects and the associated user opinions from the user text review. Although this serves as vital source information for enhancing rating prediction performance, few studies have attempted to fully utilize it for better accuracy of recommendation systems. Most of these studies typically assign equal weights to all aspects in the opinion mining process, however, in practices; users tend to give different priority on different aspects of the product when reaching overall ratings. In addition, most of the existing methods typically rely on handcrafted, rule-based or double propagation methods in the opinion mining process which are known to be time-consuming and often inclined to errors. This could affect the reliability and performance of the recommender systems (RS). Therefore, in this paper, we propose a weighted Aspect-based Opinion mining using Deep learning method for Recommender system (AODR) that can extract product's aspects and the underlying weighted user opinions from the review text using a deep learning method and then fuse them into extended collaborative filtering (CF) technique for improving the RS. The proposed method is basically comprised of two components: (1) Aspect-based opinion mining module which aims to extract the product aspects from the review text to generate aspect rating matrix. (2) Recommendation generation component that uses tensor factorization (TF) technique to compute weighted aspect ratings and finally infer the overall rating prediction. We evaluate the proposed model in terms of both aspect extraction and recommendation performance. Experiment results on different datasets show that our AODR model achieves better results compared to the baselines. (C) 2019 Elsevier Ltd. All rights reserved.
机译：基于方面的观点挖掘的主要目标是从用户文本评论中提取产品的方面以及相关的用户观点。尽管这是增强评级预测性能的重要信息源，但很少有研究尝试充分利用它来提高推荐系统的准确性。这些研究中的大多数通常在观点挖掘过程中的各个方面都赋予同等的权重，但是在实践中却是如此。达到总体评分时，用户倾向于在产品的不同方面给予不同的优先级。另外，大多数现有方法在意见挖掘过程中通常依赖于手工制作，基于规则或双重传播的方法，这些方法众所周知是耗时的，并且往往容易出错。这可能会影响推荐系统（RS）的可靠性和性能。因此，在本文中，我们提出了一种使用推荐系统深度学习方法（AODR）的基于方面的加权观点挖掘方法，该方法可以使用深度学习方法从评论文本中提取产品的方面和潜在的加权用户观点，然后将其融合为扩展协作过滤（CF）技术，用于改进RS。所提出的方法主要包括两个部分：（1）基于方面的观点挖掘模块，旨在从评论文本中提取产品方面，以生成方面等级矩阵。 （2）使用张量因子分解（TF）技术来计算加权纵横比等级并最终推断总体等级预测的建议生成组件。我们从方面提取和推荐性能两个方面评估提出的模型。在不同数据集上的实验结果表明，与基线相比，我们的AODR模型取得了更好的结果。 （C）2019 Elsevier Ltd.保留所有权利。

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