Abstract
Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users' diversified interest and its categories by analysing their implicit user profile. Depending on users' interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users' diversified interest. The performance of the proposed approach for predicting overall users' interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users' interest categories.
Highlights
With the advancement of World Wide Web (WWW), news reading style of people has rapid developments in Internet, and news reading pattern of people has slowly changed from the traditional print model to the Internet [1]
For mitigating this problem of information overload, news recommendation system plays a vital role for news portals. e news recommendation has certain unique challenges [2] compared to other domain recommendations such as item, movie, and music as the relevance of news articles may change rapidly with a short interval time relating to every recent event happening around the world
The news sites update the news articles immediately every time, but some news articles may be outdated by breaking news on the same topic multiple times persistently throughout the day requiring a constant update in the recommendation process
Summary
With the advancement of World Wide Web (WWW), news reading style of people has rapid developments in Internet, and news reading pattern of people has slowly changed from the traditional print model to the Internet [1]. It is a tough task for news readers to identify desirable news articles relevant to them [2]. For mitigating this problem of information overload, news recommendation system plays a vital role for news portals. E news recommendation has certain unique challenges [2] compared to other domain recommendations such as item, movie, and music as the relevance of news articles may change rapidly with a short interval time relating to every recent event happening around the world. Individuals in news article reading are topic-sensitive; as a result, they are usually interested in several news categories that include entertainment and sports. Individuals in news article reading are topic-sensitive; as a result, they are usually interested in several news categories that include entertainment and sports. us, prediction of users’ interest on those categories based on their reading habits is a challenging task
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
