Deep Learning Based Matrix Factorization For Collaborative Filtering

Abstract

Collaborative Filtering based on matrix factorization (MF) has shown tremendous success in the field recommender system. However, MF has difficulty in handling sparsity and scalability. These resulted in low quality of recommendations. In this regard, deep learning has shown immense success in different application areas including recommender systems. To address the limitations, we incorporate deep learning architecture to matrix factorization and develop a novel mode. The core idea of the method is to map users and items input vector to two well-structured deep neural network architectures separately for factorization. Then, we incorporate inner product to the output layers of the network to predict the rating scores. The use of this structure significantly improve the quality of recommendation. The experimental result on real data sets shows that our proposed model outperformed state of the art methods.