A multi-attention deep neural network model base on embedding and matrix factorization for recommendation

Abstract

Matrix factorization is a popular method in recommendation system. However, the quality of recommendation algorithm based on matrix decomposition is greatly affected by the sparsity of rating data. This paper presents a multi-attention deep neural network model base on Embedding and matrix factorization for recommendation. By integrating user / item embedding representation and matrix factorization representation, data sparsity and cold start problems can be effectively alleviated. The scalability of model can be solved by using the deep neural network via GPU technology. The experimental results on real data sets show that, with other classical matrix factorization recommendation algorithms, the proposed algorithm can produce higher prediction results and effectively improve the recommendation quality and performance.