Urdu News Article Recommendation Model using Natural Language Processing Techniques
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | |
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| _version_ | 1866917763893690368 |
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| author | Abbas, Syed Zain Rahman, Arif ur Mughal, Abdul Basit Haider, Syed Mujtaba |
| author_facet | Abbas, Syed Zain Rahman, Arif ur Mughal, Abdul Basit Haider, Syed Mujtaba |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_11862 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Urdu News Article Recommendation Model using Natural Language Processing Techniques Abbas, Syed Zain Rahman, Arif ur Mughal, Abdul Basit Haider, Syed Mujtaba Information Retrieval Computation and Language Machine Learning 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. |
| title | Urdu News Article Recommendation Model using Natural Language Processing Techniques |
| topic | Information Retrieval Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2206.11862 |