Collaborative filtering and recommendation algorithm based on matrix factorization and user nearest neighbor model
Xiong Lei · Journal of Computer Applications · 2012
Concerning the difficulty of data sparsity and new user problems in many collaborative recommendation algorithms,a new collaborative recommendation algorithm based on matrix factorization and user nearest neighbor was proposed.To guarantee the prediction accuracy of the new users,the user nearest neighbor model based on user data and profile information was used.Meanwhile,large data sets and the problem of matrix sparsity would significantly increase the time and space complexity.Therefore,matrix factorization was introduced to alleviate the effect of data problems and improve the prediction accuracy.The experimental results show that the new algorithm can improve the recommendation accuracy effectively,and solve the problems of data sparsity and new user.