#5327. PNTRS: Personalized news and tweet recommendation system

August 2026publication date
Proposal available till 13-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Library and Information Sciences;
Strategy and Management;
Information Systems;
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Abstract:
A news recommendation system not only must recommend the latest, trending, and personalized news to the users but also give opportunity to know about the people’s opinion on trending news. Most of the existing news recommendation systems focus on recommending news articles based on user-specific tweets. In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article. It firstly generates news recommendation based on user’s interest and twitter profile using the Multinomial Na?ve Bayes (MNB) classifier. Further, the system uses these recommended articles to recommend various trending tweets using fuzzy inference system. Additionally, feedback-based learning is applied to improve the efficiency of the proposed recommendation system. The user feedback rating is taken to evaluate the satisfaction level, and it is 7.9 on the scale of 10.
Keywords:
Feedback-Based Learning; Fuzzy Inference System; Information Filtering; Social Media; Twitter; User Preferences

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