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.
Highlights
A news article is not just a fact or information, but it is the information that affects people
In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article
The user preferences, user twitter profile, and news articles are the inputs for Personalized News Recommendation System (PNRS), and it provides personalized recommendations for news articles to the individual user as output
Summary
A news article is not just a fact or information, but it is the information that affects people. User needs to find the precise, trending and specific news which interest them They may be interested in the discussions and opinions of other people on the current news articles. The news article recommendations considering the latest twitter trends and user preferences will make it convenient for users to get relevant information This will help users to gain knowledge about the current issues and allow them to read the opinion and viewpoints of other people. In contrast to these, proposed work recommend trending news article which are personalized for specific user and recommends the tweets which are related to the recommended news article. The rest of the paper is organized in sections named as related work, methodology, experimental results and conclusions
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