Personalized Recommender System for Arabic News on Twitter

Reading online news is the most popular way to read articles from news sources worldwide. Nowadays, we have observed a mass increase of information that is shared through social media and specially news. Many researchers have proposed different techniques that focus on providing recommendations to news articles, but most of these researches focused on presenting solution for English text. This research aimed to develop a personalized news recommender system that can be used by Arabic newsreaders; to display news articles based on readers’ interests instead of presenting them only in order of their occurrence. To develop the system we have created an Arabic dataset of tweets and a set of Arabic news articles to serve as the source of recommendations. Then we have used CAMeL tools for Arabic natural language processing to preprocess the collected data. After that, we have built a hybrid recommender system through combining two filtering approaches: First, using a content-based filtering approach to consider the user's profile to recommend news articles to the user. Second, using collaborative filtering approach to consider the article's popularity with the support of Twitter. The system’s performance was evaluated using two evaluation metrics. We have conducted a user experimental study of 25 respondents to perform an assessment to get the users’ feedbacks. Also, we have used Mean Absolute Error (MAE) metrics as another way to evaluate the system accuracy. Based on evaluation results we found that hybrid recommender systems would recommend more relevant articles to users compared to the other two types of recommender system.

@article{Almotairi2021,
  title     = {Personalized Recommender System for Arabic News on Twitter},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Bashaier Almotairi and Mayada Alrige and Salha Abdullah},
  doi       = {10.14569/IJACSA.2021.0121248},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121248}
}