According to the advent of technology and the expansion of using the World Wide Web, there has been an enormous increase in the number of Internet retailers and their customers. The amount of data is exploding due to the numerous users and the variety of products. In such a condition, recommender systems play an important role in helping users and providing suggestions based on their taste. Using recommender systems’ suggestions, users can save their time on finding their personalized, useful, and favorite items without being overwhelmed by a large set of items. User-based collaborative filtering recommender systems for each user find similar users based on their ratings and suggest their favorite products to the intended user. In this paper, we present a model to improve recommender systems by finding similar users based on their reviews in addition to their ratings. To this end, we compute users’ reviews similarity utilizing seven different approaches, out of which two techniques are lexical-based, two techniques benefit from the neural representation of words, and three techniques are based on the neural representation of texts. Our experiments on two different datasets from Amazon show that the proposed model for capturing review’s similarity significantly improved the performance of the recommender system. Moreover, the model based on Long Short Term Memory (LSTM) network achieves the best results.

根据技术的出现和使用万维网的扩展，互联网零售商及其客户的数量已大大增加。由于用户众多且产品种类繁多，数据量呈爆炸式增长。在这种情况下，推荐系统在帮助用户和根据他们的口味提供建议方面起着重要作用。使用推荐系统的建议，用户可以节省时间查找个性化，有用和喜欢的项目，而不会被大量项目淹没。每个用户的基于用户的协作式过滤推荐系统会根据他们的评分找到相似的用户，并向目标用户推荐他们喜欢的产品。在本文中，我们提出了一个模型，通过根据评论和评分来查找相似的用户来改善推荐系统。为此，我们使用七种不同的方法来计算用户的评论相似度，其中两种技术基于词法，两种技术受益于单词的神经表示，而三种技术则基于文本的神经表示。我们对来自亚马逊的两个不同数据集的实验表明，所提出的用于捕获评论相似性的模型显着提高了推荐系统的性能。此外，基于长短期记忆（LSTM）网络的模型达到了最佳效果。两种技术得益于单词的神经表示，而三种技术则基于文本的神经表示。我们对来自亚马逊的两个不同数据集的实验表明，所提出的用于捕获评论相似性的模型显着提高了推荐系统的性能。此外，基于长短期记忆（LSTM）网络的模型达到了最佳效果。两种技术得益于单词的神经表示，而三种技术则基于文本的神经表示。我们对来自亚马逊的两个不同数据集的实验表明，所提出的用于捕获评论相似性的模型显着提高了推荐系统的性能。此外，基于长短期记忆（LSTM）网络的模型达到了最佳效果。