Content Based News Recommendation Engine using Hybrid BiLSTM-ANN Feature Modelling
Sashank Sridhar, Sowmya Sanagavarapu · 2021
Recommendation systems are widespread with the big data hosted on the internet and the users who actively access them. The recommendation model implemented here uses the weights of a trained Bi-LSTM ANN model that was used for multiclass news categorization and feeds these weights to a k-NN clustering algorithm. The k-NN based recommendation engine uses cosine similarity between the gradient activation maps for identifying the articles with highest similarity to the others to feed them as suggestions to the user on the platform. The implemented recommendation system is evaluated using coverage and cosine similarity analysis performed on the testing dataset. It has been observed from the coverage analysis that on an average 74.26% of news articles in the testing data have been covered for the chosen ‘n’ recommendations. The cosine similarity between different categories in the dataset is calculated and it is found that the ‘Science’, the highest score of similarity with other categories at 0.772.