The research based on the Matrix Factorization recommendation algorithms
Chen Li, Cheng Zhong Yang · 2016
Recommendation system has been placed much emphasis by researchers and programmers to deal with the information overload. Collaborative filtering algorithm is the most commonly used one. In order to enhance its performance, the Matrix Factorization was discovered to base the collaborative filtering. This paper elaborates on the collaborative filtering algorithm based on Matrix Factorization and gives a brief introduction of the gradient descend technique adopted by Matrix Factorization. Then the paper shows the whole procedures of Basic MF (Matrix Factorization), Regularized MF and Biases MF. The experiment result of collaborative recommendation algorithms based on three types of MF have been analyzed and compared, helping determine the evaluation of the number of latent factors K, the time of iteration K and regularization coefficient λ and optimizing the accuracy of algorithms.