论文标题
乌尔都语新闻文章建议模型使用自然语言处理技术
Urdu News Article Recommendation Model using Natural Language Processing Techniques
论文作者
论文摘要
乌尔都语中有几家在线报纸，但是对于用户来说，很难找到他们想要的内容，因为其中大多数包含无关的数据，并且大多数用户没有得到他们想要检索的内容。我们提出的框架将有助于为用户的利益预测乌尔都语新闻，并减少用户寻找新闻的时间。为此，使用NLP技术进行预处理，然后使用具有余弦相似性的TF-IDF来获得最高的相似性和用户偏好的推荐新闻。此外，BERT语言模型还用于相似性，并且通过使用BERT模型相似性与TF-IDF相比增加，因此该方法与BERT语言模型的效果更好，并以他们的兴趣向用户推荐新闻。当文章的相似性超过60％时，建议发布新闻。
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.
