There are several online newspapers in urdu but for the users it is difficult
to find the content they are looking for because these most of them contain
irrelevant data and most users did not get what they want to retrieve. Our
proposed framework will help to predict Urdu news in the interests of users and
reduce the users searching time for news. For this purpose, NLP techniques are
used for pre-processing, and then TF-IDF with cosine similarity is used for
gaining the highest similarity and recommended news on user preferences.
Moreover, the BERT language model is also used for similarity, and by using the
BERT model similarity increases as compared to TF-IDF so the approach works
better with the BERT language model and recommends news to the user on their
interest. The news is recommended when the similarity of the articles is above
60 percent.

提出一种可以预测用户兴趣的乌尔都语新闻推荐框架，使用了 NLP 技术的预处理和 TF-IDF 和余弦相似度计算，利用 BERT 语言模型相似性提高了系统推荐性能。当文章相似性超过 60％时，系统会向用户推荐相关新闻。