Matrix factorization recommendation algorithm based on deep neural network

摘要

Collaborative filtering is the most classical technology in recommendation system. Compared with memory-based collaborative filtering technology, matrix factorization has good scalability and recommendation effect, which makes it widely used. On the basis of matrix factorization model, deep neural network is introduced to improve the accuracy of scoring prediction and the quality of recommendation. Experiments on MovieLens dataset show that the proposed method improves the accuracy and quality of recommendation algorithm.