Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news recommendation systems allow us to filter content and deliver it to the user in proportion to his desires and interests. RSs have three techniques: content-based filtering, collaborative filtering, and hybrid filtering. We will use the MIND dataset with our system, which was collected in 2019, the big challenge in this dataset because there is a lot of ambiguity and complex text processing. In this paper, will present our proposed recommendation system. The core of our system we have used the GloVe algorithm for word embeddings and representation. Besides, the Multi-head Attention Layer calculates the attention of words, to generate a list of recommended news. Finally, we achieve good results more than some other related works in AUC 71.211, MRR 35.72, nDCG@5 38.05, and nDCG@10 44.45.

翻译：目前,这是研究人员和其他个人实现自身利益的一个非常重要的方法, 因为它提供了满足其需求的短期解决方案。 由于互联网上的信息数量众多, 新闻推荐系统允许我们过滤内容, 并根据用户的愿望和利益向用户发送信息。 RS有三种技术: 基于内容的过滤、 合作过滤和混合过滤。 我们将使用2019年收集的MIND系统数据集, 这个数据集的巨大挑战在于大量模糊和复杂的文本处理。 本文将介绍我们拟议的建议系统。 我们系统的核心是用 GloVe 算法嵌入和表达文字。 此外, 多头注意层计算了文字的注意度, 以生成推荐新闻的清单。 最后, 我们取得了优于AUC 71. 211、 MRR 35.72、 nDCG@5 38.05和 nDCG@ 10 44. 45 中的其他相关作品。