Tackling data sparsity: a hybrid filtering paradigm for robust recommender systems

This paper introduces a hybrid recommender system approach that aims to tackle the problems associated with data sparsity, also referred to as the 'cold start problem', Recommender systems use user preferences to filter information. To improve recommendation accuracy, our method combines user-based and content-based collaborative filtering techniques. More specifically, content-based filtering takes over when there is little data. When there is a high degree of user similarity, user-based collaborative filtering is used to maximise accuracy by suggesting diverse items. This strategy can be used in a variety of fields, including e-commerce, music, books, and film.