Various recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

近年来已经开发了各种推荐系统（RS），其中许多都集中在英语内容上。因此，对文献中的大多数RS进行了英语内容比较。然而，当使用其他语言（如阿拉伯语）的内容时，有关RS的研究调查很少。研究人员仍然忽略了阿拉伯RS领域。因此，我们通过本研究的目的是通过利用英语RSs领域的最新进展来填补这一研究空白。我们的主要目标是在阿拉伯语环境下调查最近的RS。为此，我们首先选择了五个专门针对英语内容的最新RS，然后根据经验评估了它们在阿拉伯语内容上的表现。由于这项工作，我们首先建立四个可公开获取的大规模阿拉伯数据集以进行推荐。其次，已经提供了各种文本预处理技术来准备构造的数据集。第三，我们的调查得出了有关阿拉伯语中现代RS用法的公认观点。实验结果证明，这些系统在应用于阿拉伯文内容时可确保高性能。