Aiming at the problem that the neighbor-based collaborative filtering algorithm only considers the similarity factor and the negative value of the traditional matrix decomposition

a two-stage K-nearest neighbor selection algorithm combining non-negative matrix factorization and project heat is proposed. The project heat is integrated into the similarity calculation

the problem of data sparsity is effectively alleviated. Experimental results show that the proposed algorithm has higher precision than the classical algorithm.