An attention-based deep learning method for solving the cold-start and sparsity issues of recommender systems

Narges Heidari, Parham Moradi, Abbas Koochari

Abstract: Highlights•An attention-based deep learning recommender system called ADLRS is proposed.•ADLRS incorporates profiles of items with the framework of matrix factorization.•The BERT language model is used to represents item profiles in the form of vectors.•A deep autoencoder is used to extract effective features and reduce the dimensionality of vectors.•An iterative method is used to solve the objective function and predict unknown ratings.