Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF ¹ vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.

中文翻译：

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基于评论的推荐系统：使用评论的词嵌入表示的拟议等级预测方案

如今，推荐系统在为用户（尤其是电子商务应用程序）提供有用信息方面起着重要作用。许多建议的模型使用用户的评分历史记录来预测未知的评分。最近，用户评论作为有价值的知识来源已经吸引了该领域研究人员的注意力，并且出现了一种新的类别，称为基于评论的推荐系统。在这项研究中，我们利用用户评论中包含的信息以及可用的评分分数来开发基于评论的评分预测系统。所提出的方案试图通过模糊给定的等级来处理等级历史的不确定性问题。提议的系统的另一个优势是使用文字嵌入表示模型进行文字评论，^1个向量空间。它还利用帮助投票分数，以修剪数据并获得更好的结果。针对亚马逊数据集评估了评级预测方案以及最终推荐系统的有效性。实验结果表明，所提出的推荐系统性能优于同类推荐系统，可以用作电子商务环境中的合适工具。