A survey of contend-based filtering technique for personalized recommendations
Lefats’e Manamolela, Martin Appiah, Tranos Zuva · 2020
The number of choices on the internet is devastating. In recent years, internet services have grown exponentially and it is unavoidable that a number of services suffer from information overload, which makes it difficult for users to find the information they are in need of. This is where recommender systems or RSs come in handy by delivering personalized suggestions from a set of choices that is presented to users to pinpoint items or services of interest. Recommender systems among other practices are mostly used for music, movies, jokes, restaurants, financial services, life insurance, Instagram Facebook and twitter followers. This makes recommender systems a fundamental part of websites and e-commerce applications. In this paper, we present a survey of content-based filtering recommender systems. We further provide the landscape of different recommendation methods and their basic approaches.