The advancement in the production, distribution, and consumption of news has fostered easy access to the news with fair challenges. The main challenge is to present the right news to the right audience. The news recommendation system is one of the technological solutions to this problem. Much work has been done on news recommendation systems for the major languages of the world, but trivial work has been done for resource-poor languages like Urdu. Another significant hurdle in the development of an efficient news recommendation system is the scarcity of an accessible and suitable Urdu dataset. To this end, an Urdu news mobile application was used to collect the news data and user feedback for 1 month. After refinement, the first-ever Urdu dataset of 100 users and 23,250 news was curated for the Urdu news recommendation system. In addition, SEEUNRS, a semantically enriched entity-based Urdu news recommendation system, is proposed. The proposed scheme exploits the hidden features of a news article and entities to suggest the right article to the right audience. Results have shown that the presented model has an improvement of 6.9% in the F1 measure from traditional recommendation system techniques.

新闻生产、传播和消费的进步促进了新闻获取的便捷性和公平性的挑战。主要挑战是向正确的受众呈现正确的新闻。新闻推荐系统就是解决这一问题的技术方案之一。在世界主要语言的新闻推荐系统方面已经做了很多工作，但对于乌尔都语等资源匮乏的语言却做了一些琐碎的工作。开发高效新闻推荐系统的另一个重大障碍是缺乏可访问且合适的乌尔都语数据集。为此，使用乌尔都语新闻移动应用程序收集了1个月的新闻数据和用户反馈。经过细化，为乌尔都语新闻推荐系统策划了第一个包含 100 个用户和 23,250 条新闻的乌尔都语数据集。此外，还提出了SEEUNRS，一种语义丰富的基于实体的乌尔都语新闻推荐系统。所提出的方案利用新闻文章和实体的隐藏特征向正确的受众推荐正确的文章。结果表明，所提出的模型在 F1 度量上比传统推荐系统技术提高了 6.9%。