Abstract Content-based filtering (CBF), one of the most successful recommendation
techniques, is based on correlations between contents. CBF uses item information …

Recommendation systems have been tremendously important to assist users to find relevant
items. With the information-overloaded problem, it becomes crucial to understand users' …

Recommender systems are being increasingly used to predict the preferences of users on
online platforms and recommend relevant options that help them cope with information …

Today's internet consists of an abundant amount of information that makes it difficult for
recommendation engines to produce satisfying outputs. This huge stream of unrelated data …

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict
the ratings for unseen items with high accuracy. Matrix factorization is an accurate …

Recommender systems recommend items to users based on their interests and have seen
tremendous growth due to the use of internet and web services. Recommendation systems …