PNTRS: Personalized News and Tweet Recommendation System

Sunita Tiwari, Sushil Kumar, Vikas Jethwani, Deepak Kumar, Vyoma Dadhich

Source Title: Journal of Cases on Information Technology (JCIT)24(3)

ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878223|DOI: 10.4018/JCIT.20220701.oa9

MLA

Tiwari, Sunita, et al. "PNTRS: Personalized News and Tweet Recommendation System." JCIT vol.24, no.3 2022: pp.1-19. http://doi.org/10.4018/JCIT.20220701.oa9

APA

Tiwari, S., Kumar, S., Jethwani, V., Kumar, D., & Dadhich, V. (2022). PNTRS: Personalized News and Tweet Recommendation System. Journal of Cases on Information Technology (JCIT), 24(3), 1-19. http://doi.org/10.4018/JCIT.20220701.oa9

Chicago

Tiwari, Sunita, et al. "PNTRS: Personalized News and Tweet Recommendation System," Journal of Cases on Information Technology (JCIT) 24, no.3: 1-19. http://doi.org/10.4018/JCIT.20220701.oa9

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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 Naïve 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.