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.
Classification of label-specific users' diversified interests and incorporating social media with news media to address popular news articles which is the most formidable task in popularity-based personalized news recommendation systems (PPNRS). To bring personalization to PPNRS, many remarkable features have to be considered from users user profiles to classify their interest. In this article, 13,346 features per user considered to classify their interest for 15 labels using multi-label convolutional neural network (MLCNN). The efficiency of MLCNN model highly depends on its architecture through the tuning of its hyperparameters. Generally, researchers manually designed a constant CNN architecture for every label and verified the effectiveness, but this leads to an additional complexity as well as large computational resources were consumed.Moreover, designing the structure for all 15 labels leads to an increase in the network structure exponentially with an increase in labels. Hence, in this manuscript, MLCNN architectures optimized by implementing a novel approach modified genetic algorithm (MGA) with the help of introducing four novel crossover operators to strengthen CNN performance for users interest classification. Further, for the recommendation process, the label-specific news articles were clustered from social media Facebook and Twitter feeds, and then most popular news articles determined from clusters along with label-specific breaking news articles rendered from news feeds concerning users' interest. In addition to that, the reliability of the news articles also validated for recommendation process. The experimental result precisely proves that the proposed approach MGA attained an accuracy of 89.64%, 90.56%, 90.41%, and 91.79% for classifying users label specific interest and label-wise recommendation accuracy attained 93.3%, 90%, 90% from Twitter, Facebook, and also from Newsfeed respectively.
Classification of label-specific users’ diversified interests is the most formidable task in personalized news recommendation systems (PNRS). To bring personalization to PNRS, many remarkable features have to be considered from their user profile to classify their interest. In this paper, 13, 346 features are considered per user to classify their interest for 15 labels using Multi-label Convolution Neural Network (MLCNN). The efficiency of MLCNN highly depends on its architecture through the tuning of its hyper parameters. Generally, researchers have manually designed a constant CNN architecture for each label and every label and verified the effectiveness, but this leads to additional complexity as well as large computational resources were consumed. Moreover, Designing the structure for all 15 labels leads to an increase in network structure exponentially with an increase in labels. Hence, in this paper, MLCNN architectures are optimized by implementing a novel approach Modified Genetic Algorithm (MGA) with the help of introducing four novel crossover operators to strengthen CNN performance for users interest classification. Further, for the recommendation process, the label-specific news articles were clustered from social media Facebook and Twitter feeds, and then most popular news articles were determined along with label-specific breaking news articles rendered from news feeds concerning users’ interest. The experimental result precisely proves that the proposed approach MGA attained an accuracy of 89.64%, 90.56%, 90.41%, and 91.79% for classifying users label specific interest and label-wise recommendation accuracy attained 93.3%, 90%, 90% from Twitter, Facebook, and also from Newsfeed respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.