Optimized multi‐label convolutional neural network using modified genetic algorithm for popularity based personalized news recommendation system

Abstract

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.

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“…When compared to the standard method, the one proposed yields better results. Accuracy for CA-LDA [34] is at 57%, KNN [35] at 64%, MLCNN [36] at 77%, IKCD [37] at 85%, and the proposed INBCA at 93%. Accuracy is measured in many ways, but one of the most crucial is precision.…”

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“…To evaluate the performance of the proposed algorithm, the algorithm of this model is compared with the GCN [50], Graph Attention Network (GAT) [51], CNN [52] Figure 10 illustrates the runtime of different algorithms under varying data sizes. It is observed that as the data size increases, each algorithm's runtimetends to grow.…”